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<html>	===== See relevant faculty pages for publications =====
	[[http://www.robots.ox.ac.uk/~sjrob/\|Stephen Roberts]]\\
<div class="topshoot">	[[http://www.robots.ox.ac.uk/~mosb/\|Michael Osborne]]\\
Jump to: [<a HREF="#tag1994">1994</a>] [<a HREF="#tag1995">1995</a>]	[[http://www.robots.ox.ac.uk/~xdong/\|Xiaowen Dong]]\\
[<a HREF="#tag1996">1996</a>] [<a HREF="#tag1997">1997</a>]	[[http://www.robots.ox.ac.uk/~jan/\|Jan-Peter Calliess]]\\
[<a HREF="#tag1998">1998</a>] [<a HREF="#tag1999">1999</a>]	[[https://scholar.google.co.uk/citations?user=mtNQD-8AAAAJ&hl=en\|Stefan Zohren]]
[<a HREF="#tag2000">2000</a>] [<a HREF="#tag2001">2001</a>]
[<a HREF="#tag2002">2002</a>] [<a HREF="#tag2003">2003</a>]
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[<a HREF="#tag2006">2006</a>] [<a HREF="#tag2007">2007</a>]
[<a HREF="#tag2008">2008</a>] [<a HREF="#tag2009">2009</a>]
[<a HREF="#tag2010">2010</a>] [<a HREF="#tag2011">2011</a>]
[<a HREF="#tag2012">2012</a>] [<a HREF="#tag2013">2013</a>]
[<a HREF="#tag2014">2014</a>] [<a HREF="#tag2015">2015</a>]
[<a HREF="#tag2016">2016</a>] [<a HREF="#tag2017">2017</a>]
[<a HREF="#tag2018">2018</a>] [<a HREF="#tag2019">2019</a>]
[<a HREF="#tag2020">2020</a>] [<a HREF="#tag2021">2021</a>]
[<a HREF="#tag2022">2022</a>]
[<a HREF="#tagtheses">theses</a>]
</div>
<BR>

<H3><a NAME="tag2022">2022</a></H3>
<UL>

<LI>E. Cetin, P. Ball, S. Roberts, O. Celiktutan (2022).<BR>
Stabilizing Off-Policy Deep Reinforcement Learning from Pixels.
<I>ICML 2022.</I>

<LI>Andrea Patane, Arno Blaas, luca laurenti, Luca Cardelli, Stephen
Roberts, Marta Kwiatkowska (2022).<BR>
<a href="https://jmlr.org/papers/v23/21-0382.html">Adversarial Robustness Guarantees for Gaussian Processes.</a>
<I>Journal of Machine Learning Research.</I>.

<LI>Daniel Poh, Stephen Roberts and Stefan Zohren (2022).<BR>
Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention. <I>The Journal of Financial Data Science.</I>

<LI>Shuyu Lin, Ronald Clarke, Niki Trigoni and Stephen Roberts
(2022).<BR>
Uncertainty Estimation With a VAE-Classifier Hybrid Model. <I>Proceedings of ICASSP 2022.</I>

<LI>S. Cameron, T. Cameron, A. Pretorius and S. Roberts (2022).<BR>
Robust and Scalable SDE Learning: A Functional Perspective. <I>Proceedings of ICLR 2022.</I>

<LI>Cong Lu, Philip J. Ball, Jack Parker-Holder, Michael A. Osborne,
Stephen J. Roberts (2022).<BR>
Revisiting Design Choices in Offline Model Based Reinforcement Learning. <I>Proceedings of ICLR 2022.</I>

<LI>Saad Hamid, Sebastian Schulze, Michael A. Osborne, Stephen
J. Roberts (2022).<BR>
Marginalising over Stationary Kernels with Bayesian Quadrature. <I>Proceedings of AISTATS 2022.</I>

<LI>Samuel Kessler, Jack Parker-Holder, Philip Ball, Stefan Zohren,
Stephen J. Roberts (2022). <BR>
Same State, Different Task: Continual Reinforcement Learning without
Interference.
<I>Proceedings of AAAI 2022.</I> arXiv:2106.02940

<LI>Jaleh Zand, Jack Parker-Holder and Stephen Roberts (2022).<BR>
On-the-fly Strategy Adaptation for ad-hoc Agent Coordination.
<I>Proceedings of AAMAS 2022.</I>

<LI>B. Damghani, S. Roberts (2022).<BR>
Guidelines for Building a Realistic Algorithmic Trading Market
Simulator for Backtesting while Incorporating Market Impact. <I>Algorithmic Finance.</I>

</UL>


<H3><a NAME="tag2021">2021</a></H3>
<UL>

<LI>S Kessler, A Cobb, S Zohren, SJ Roberts (2021).<BR>
<a href="https://openreview.net/pdf?id=2Ann7eaLBEv">Can Sequential
Bayesian Inference Solve Continual Learning?</a>
4th Symposium on Advances in Approximate Bayesian Inference, 2021 1-18

<LI>Shaan Desai, Marios Mattheakis, Hayden Joy, Pavlos Protopapas, Stephen Roberts (2021).<BR>
<a href="https://arxiv.org/abs/2110.11286">One-Shot Transfer Learning of Physics-Informed Neural Networks.</a> https://arxiv.org/abs/2110.11286

<LI>Kieran Wood, Sven Giegerich, Stephen Roberts, Stefan Zohren
(2021).<BR>
<a href="http://arxiv.org/abs/2112.08534">Trading with the Momentum
Transformer: An Intelligent and Interpretable Arc\
hitecture.</a>

<LI>Martin Tegner and Stephen Roberts (2021).<BR>
<a href="http://arxiv.org/abs/2112.03718">A Bayesian take on option
pricing with Gaussian processes.</a>
<I>NeurIPS Workshop on Machine Learning meets Econometrics
(MLECON).</I>

<LI>Martin Tegner and Stephen Roberts (2021).<BR>
A Probabilistic Approach to Nonparametric Local Volatility.
<I>Journal of Computational Finance</I>, 25(3).
(https://doi.org/10.21314/JCF.2021.012)

<LI>I. Kiskin et al. (2021).<BR>
HumBugDB: A Large-scale Acoustic Mosquito Dataset. <i>NeurIPS
2021.</i>

<LI>Jack Parker-Holder, Vu Nguyen, Shaan Desai, S Roberts (2021). <BR>
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population
Based AutoRL. <i>NeurIPS 2021.</i>.

<LI>Shaan A. Desai, Stephen J. Roberts and Marios Mattheakis
(2021).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/vign_2021.pdf">Variational
integrator graph networks for learning energy conserving dynamical systems.</a> <i>Physical Review E</i> (to
appear).

<LI>Shaan A. Desai, Stephen J. Roberts, Marios Mattheakis, David
Sondak and Pavlos Protopapas (2021).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/pHNN_2021.pdf">Port-Hamiltonian
neural networks for learning explicit time-dependent dynamical systems.</a> <i>Physical Review E</i> (to appear).

<LI>Jaleh Zand and Stephen Roberts (2021).<BR>
<a href="https://www.mdpi.com/2624-6120/2/3/34">
Mixture Density Conditional Generative Adversarial Network Models
(MD-CGAN).</a><BR>
<I>Signals</I> Vol 2, no. 3, 559-569. Special Issue on Machine Learning.

<LI>Martin Tegner and Stephen Roberts (2021).<BR>
A Probabilistic Approach to Nonparametric Local Volatility.
<I>Journal of Computational Finance.</I> (to appear).

<LI>Cong Lu, Phillip Ball, Jack Parker-Holder, Michael Osborne and Stephen Roberts (2021).<BR>
Revisiting Design Choices in Offline Model Based Reinforcement Learning. <I>Reinforcement Learning for Real Life Work\
shop, ICML 2021 (spotlight paper).</I>

<LI>Jack Parker-Holder, Vu Nguyen, Shaan Desai, Stephen Roberts
(2021).<BR>
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population
Based AutoRL. https://arxiv.org/abs/2106.15883

<LI>Scott Cameron, Stephen Roberts (2021).<BR>
Governing equation construction for critical transitions in Langevin type NeuralSDEs.<BR>
<I>16th International Conference on Dynamical Systems - Theory and Applications.</I>

<LI>Ivan Kiskin, Adam Cobb, Marianne Sinka, Kathy Willis and Stephen Roberts (2021).<BR>
Automatic acoustic mosquito tagging with Bayesian neural networks. <I>ECML-PKDD 2021.</i>

<LI>Philip J. Ball, Cong Lu, Jack Parker-Holder, Stephen Roberts (2021).<BR>
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment.<BR>
<I>ICLR 2021 Reinforcement Learning Workshop</I>. https://arxiv.org/abs/2104.05632
https://www.youtube.com/watch?v=KcG2hz9tZsQ&t=8s

<LI>Sinka, M. E., Zilli, D. Li, Y., Kiskin, I., Kirkham, D, Rafique, W.,
Wang, L., Chan, H., Gutteridge, B., Herreros-Moya, E., Portwood, H.,
Roberts, S. and Willis, K. J. (2021).<BR>
HumBug – An Acoustic Mosquito Monitoring Tool for use on budget
smartphones. <I>Methods in Ecology and Evolution</I>. DOI:
10.1111/2041-210X.13663

<LI>Saad Hamid, Sebastian Schulze, Michael A. Osborne, Stephen J. Roberts (2021).<BR>
Marginalising over Stationary Kernels with Bayesian Quadrature.
arXiv:2106.07452

<LI>Samuel Kessler, Jack Parker-Holder, Philip Ball, Stefan Zohren, Stephen J. Roberts (2021). <BR>
Same State, Different Task: Continual Reinforcement Learning without Interference. arXiv:2106.02940

<LI>Kieran Wood, Stephen Roberts, Stefan Zohren (2021).<BR>
Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection.<BR>
https://arxiv.org/abs/2105.13727. <I>Journal of Financial Data Science (to appear) Volume 4, Issue 1. </I>

<LI>Daniel Poh, Bryan Lim, Stefan Zohren and Stephen Roberts. (2021).<BR>
Enhancing Cross-Sectional Currency Strategies by Ranking Refinement with Transformer-based Architectures. <BR>
https://arxiv.org/abs/2105.10019

<LI>Samuel Kessler, Vu Nguyen, Stefan Zohren, Stephen Roberts (2021).<BR>
Hierarchical Indian Buffet Neural Networks for Bayesian Continual Learning.<BR>
<I>Proceedings of UAI 2021.</I> (to appear). https://arxiv.org/pdf/1912.02290.pdf

<LI>Aldo Pacchiano, Philip Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts (2021). <BR>
Towards Tractable Optimism in Model-Based Reinforcement Learning.<BR>
<I>Proceedings of UAI 2021.</I> (to appear). https://arxiv.org/abs/2006.11911

<LI>Philip J. Ball, Cong Lu, Jack Parker-Holder, Stephen Roberts (2021).<BR>
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment.<BR>
<I>ICML</I> (to appear). https://arxiv.org/abs/2104.05632

<LI>Kuok, S.C., and Roberts S.J. and Girolami, M. and Yuen, K.-V. (2021). <BR>
Broad Learning Robust Semi-active Structural Control: a Nonparametric Approach.<BR>
<I>Mechanical Systems and Signal Processing,</I> (in press).
https://authors.elsevier.com/c/1d5Qy39~t0Y0Ki

<LI>Wolfgang Fruehwirt, Leonhard Hochfilzer, Leonard Weydemann, Stephen Roberts (2021).<BR>
<a href="https://authors.elsevier.com/c/1czT15VD4Kgjaz">
Cumulation, Crash, Coherency: A Cryptocurrency Bubble Wavelet Analysis.</a><BR>
Finance Research Letters.

<LI>Camilla Sterud, Signe Moe, Mads Valentin Bram, Stephen Roberts and Jan Calliess (2021).<BR>
Recurrent neural network structures for learning control valve behaviour.<BR>
<I>Automation, Robotics & Communications for Industry 4.0 (ARCI' 2021)</I>

<LI>Alexander Camuto, Matthew Willetts, Brooks Paige, Chris Holmes and Stephen Roberts (2021).<BR>
Learning Bijective Feature Maps for Linear ICA.<BR>
<i>Proceedings of AISTATS 2021</i> (to appear).
https://arxiv.org/pdf/2002.07766.pdf

<LI>Alexander Camuto, Matthew Willetts, Stephen Roberts, Chris Holmes, Tom Rainforth (2021).<BR> Towards a Theoretical Understanding of the Robustness of Variational Autoencoders.<BR> <i>Proceedings of AISTATS 2021</i> (to appear). https://arxiv.org/pdf/2007.07365.pdf

<LI>Matthew Willetts, Alexander Camuto, Tom Rainforth, Stephen Roberts, Chris Holmes (2021).<BR>
Improving VAEs' Robustness to Adversarial Attack.<BR>
<I>Proceedings of ICLR 2021</I> (to appear), https://arxiv.org/abs/1906.00230

<LI>A. Aprem and S. Roberts (2021).<BR>
Optimal pricing in black box producer-consumer Stackelberg games using revealed preference feedback.<BR>
<I>Neurocomputing.</I> (to appear)

</UL>

<H3><a NAME="tag2020">2020</a></H3>
<ul>
<LI>Diego Granziol, Stefan Zohren, Stephen Roberts (2020).<BR>
<a href="https://arxiv.org/abs/2006.09092">Learning Rates as a
Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training.</a>

<li>B. Mahdavi-Damghani, K. Mustafayeva, S. Roberts and C. Buescu (2017).<BR>
Portfolio Optimization in the Context of Cointelated Pairs: Stochastic Differential Equation vs. Machine Learning Approach.<BR>
<i>Algorithmic Finance</i> (Volume 8, issue 3-4).

<LI>Daniel Poh, Bryan Lim, Stefan Zohren and Stephen Roberts (2020).<BR>
Building Cross-Sectional Systematic Strategies By Learning to Rank.<BR>
<i>Journal of Financial Data Science</i>, Volume 3, Issue 2.
https://arxiv.org/pdf/2012.07149.pdf

<LI>Arno Blaas and Stephen Roberts (2020).<BR>
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks.<BR>
<I>RSEML 2021 workshop, AAAI 2020.</I>

<LI>Diego Granziol, Samuel Albanie, Xingchen Wan, Stephen Roberts (2020).<BR>
Explaining the Adaptive Generalisation Gap.
https://arxiv.org/abs/2011.08181

<LI>Noor Sajid, Philip J. Ball, Thomas Parr and Karl J. Friston (2020).<BR>
Active inference: demystified and compared.<BR>
<I>Neural Computation.</I>
https://www.mitpressjournals.org/doi/abs/10.1162/neco_a_01357 ,
https://arxiv.org/pdf/1909.10863.pdf

<LI>Guest Editor(s): Stephen Roberts, Stefan Zohren. (2020). <BR>
<I>Entropy Based Inference and Optimization in Machine Learning</I>.<BR>
https://www.mdpi.com/journal/entropy/special_issues/optimizations_machine_learning

<LI>Matthew Willetts, Stephen Roberts, and Chris Holmes (2020)<BR>
Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels.<BR>
<I>Machine Learning for Big Data MLBD2020</i> (to appear).

<LI>Samuel Kessler, Vu Nguyen, Stefan Zohren, Stephen Roberts (2020).<BR>
Hierarchical Indian Buffet Neural Networks for Bayesian Continual Learning.<BR>
https://arxiv.org/pdf/1912.02290.pdf

<LI>Jack Parker-Holder, Vu Nguyen and Stephen Roberts (2020).<BR>
Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits.<BR>
<I>Proceedings of NeurIPS 2020 (to appear).</I>

<LI>Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts (2020).<BR>
Effective Diversity in Population Based Reinforcement Learning.<BR>
<I>Proceedings of NeurIPS 2020 (to appear).</I>

<LI>Alexander Camuto, Matthew Willetts, Umut Şimşekli, Stephen Roberts, Chris Holmes (2020).<BR>
Explicit Regularisation in Gaussian Noise Injections.<BR>
<I>Proceedings of NeurIPS 2020 (to appear).</I>

<LI>Vu Nguyen, Sebastian Schulze, Michael A. Osborne (2020).<BR>
Bayesian Optimization for Iterative Learning.<BR>
<I>Proceedings of NeurIPS 2020 (to appear).</I>

<LI>Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael A. Osborne, Frank Wood (2020).<BR>
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective.<BR>
<I>Proceedings of NeurIPS 2020 (to appear).</I>

<LI>Jack Parker-Holder, Cinjon Resnick, Luke Metz, Hengyuan Hu, Adam Lerer, Alistair HP Letcher, Alex Peysakhovich, Aldo Pacchiano, Jakob Foerster (2020).<BR>
Ridge Riding: Finding diverse solutions by following eigenvectors of the Hessian.<BR>
<I>Proceedings of NeurIPS 2020 (to appear).</I>

<LI>Kyriakos Polymenakos, Luca Laurenti, Andrea Patane, Jan-Peter Calliess, Luca Cardelli, Marta Kwiatkowska, Alessandro Abate, Stephen Roberts (2020).<BR>
Safety Guarantees for Iterative Predictions with Gaussian Processes.<BR>
<I>59th IEEE Conference on Decision and Control (CDC)</I>, 2020.

<LI>T. Spears, S. Zohren and S. Roberts (2020).<BR>
Investment Sizing with Deep Learning Prediction Uncertainties for High-Frequency Eurodollar Futures Trading.<BR>
Available at SSRN: https://ssrn.com/abstract=3664497 or http://dx.doi.org/10.2139/ssrn.3664497

<LI>S. Ghoshal, M. Bengtzen and S. Roberts (2020).<BR>
Short Memories? The Impact of SEC Enforcement on Insider Leakage.<BR>
<I>Journal of Law, Finance, and Accounting</I>: Vol. 5: No. 2, pp 273-305. http://dx.doi.org/10.1561/108.00000048

<LI> P. W. Hatfield, I. A. Almosallam, M. J. Jarvis, N.Adams, R.A.A. Bowler, Z. Gomes, S. J. Roberts, C.Schreiber (2020).<BR>
Augmenting machine learning photometric redshifts with Gaussian mixture models.<BR>
<I>Monthly notices of the Royal Astronomical Society.</I> (to appear).

<LI>D Granziol, X Wan, S Roberts (2020).<BR>
Gadam: Combining Adaptivity with Iterate Averaging Gives Greater Generalisation.<BR>
arXiv:2003.01247

<LI>Kyriakos Polymenakos, Nikitas Rontsis, Alessandro Abate and Stephen Roberts (2020).<BR>
Safe PILCO: a Software Tool for Safe and Data-Efficient Policy Synthesis.<BR>
<i>Proceedings of QEST 2020</i> (to appear).

<LI>Samuel Kessler, Arnold Salas, Vincent Tan Weng Choon, Stefan Zohren and Stephen Roberts (2020).<BR>
Practical Bayesian Neural Networks via Adaptive Subgradient Optimization Methods.<BR>
<i>ICML 2020 workshop on Uncertainty and Robustness in Deep Learning</i>

<LI>Sam Kessler, Jack Parker-Holder, Philip Ball, Stefan Zohren and Stephen Roberts (2020).<BR>
UNCLEAR: A Straightforward Method for Continual Reinforcement Learning.<BR>
<i>ICML 2020 Workshop on Continual Learning</i>.

<LI>Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski and Stephen Roberts (2020).<BR>
Effective Diversity in Population Based Reinforcement Learning.<BR>
<i>LifelongML workshop, ICML.</i>

<LI>Jack Parker-Holder, Vu Nguyen, Stephen Roberts (2020).<BR>
One-Shot Bayes Opt via Probabilistic Population Based Training.<BR>
<i>7th ICML Workshop on Automated Machine Learning (AutoML)</i>

<LI>Zihao Zhang, Stefan Zohren, Stephen Roberts (2020).<BR>
Deep Learning for Portfolio Optimisation.<BR>
<I>Journal of Financial Data Science</i> (to appear), also https://arxiv.org/abs/2005.13665

<LI>Jan-Peter Calliess, Stephen Roberts, Carl Edward
Rasmussen, Jan Maciejowski (2020).<BR>
Lazily Adapted Constant Kinky Inference for Nonparametric Regression and Model-Reference Adaptive Control.<BR>
<i>Automatica</i> (to appear)

<LI>Bryan Lim, Stefan Zohren, Stephen Roberts (2020).<BR>
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio.<BR>
<i>Risk</i> http://www.risk.net/7659971 and http://arxiv.org/abs/2002.02008

<LI>Jack Parker-Holder, Vu Nguyen and Stephen Roberts (2020).<BR>
Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits.<BR>
<i>7th ICML Workshop on Automated Machine Learning (AutoML)</i>

<LI>S. Ghoshal, M. Bengtzen and S. Roberts (2020).<br>
Short Memories? The Impact of SEC Enforcement on Insider Leakage.<BR>
Journal of Law, Finance and Accounting (to appear).

<LI>Zihao Zhang, Stefan Zohren, Stephen Roberts (2020).<BR>
Deep Learning for Portfolio Optimisation. https://arxiv.org/abs/2005.13665

*<LI>Philip Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts (2020). ( equal lead authors)<BR>**
Ready Policy One: World Building Through Active Learning.<BR>
To appear in The International Conference on Machine Learning (ICML), 2020 (ArXiv)

*<LI>Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Anna Choromanska, Krzysztof Choromanski, Michael I. Jordan (2020). (* equal lead authors) <BR>**
Learning to Score Behaviors for Guided Policy Optimization.<BR>
To appear in The International Conference on Machine Learning (ICML), 2020 (ArXiv)

<LI>Krzysztof Choromanski, David Cheikhi, Jared Davis, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou, Xingyou Song, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano, Tamas Sarlos, Adrian Weller, Vikas Sindhwani (2020). ( equal lead authors) <BR>
Stochastic Flows and Geometric Optimization on the Orthogonal Group.<BR>
To appear in The International Conference on Machine Learning (ICML), 2020 (ArXiv)

<LI>Binxin Ru, Ahsan S. Alvi, Vu Nguyen, Michael A. Osborne, Stephen J Roberts (2020).<BR>
Bayesian Optimisation over Multiple Continuous and Categorical Inputs.<BR>
To appear in The International Conference on Machine Learning (ICML), 2020 (ArXiv)

<LI>Vu Nguyen, Michael A Osborne (2020).<BR>
Knowing The What But Not The Where in Bayesian Optimization.<BR>
To appear in The International Conference on Machine Learning (ICML), 2020 (ArXiv)
<LI>Zihao Zhang, Stefan Zohren and Stephen Roberts (2020).<BR>
Deep Reinforcement Learning for Trading.<BR>
The Journal of Financial Data Science Vol 2(2) Spring 2020, jfds.2020.1.030; DOI: https://doi.org/10.3905/jfds.2020.1.030

<LI>Shaan Desai, Stephen Roberts (2020).<BR>
VIGN: Variational Integrator Graph Networks. https://arxiv.org/abs/2004.13688.

<LI>Sin-Chi Kuok , Ka-Veng Yuen, Stephen Roberts and Mark A. Girolami (2020).<BR>
Propagative broad learning for nonparametric modeling of ambient effects on structural health indicators.<BR>
Structural Health Monitoring Journal (to appear).

<LI>J. Zand and S. Roberts (2020).<BR>
Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN). https://arxiv.org/pdf/2004.03797.pdf

<LI>B. Lim, S. Zohren and S. Roberts (2020).<BR>
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction.<BR>
Proceedings of IJCNN 2020 (to appear). https://arxiv.org/pdf/1901.08096.pdf

<LI>S. Ghoshal and S. Roberts (2020).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/Ghoshal_et_al-2020-Neural_Computing_and_Applications.pdf">
Thresholded ConvNet Ensembles: Neural Networks for Technical Forecasting.</a><BR>
Neural Computing and Applications. https://doi.org/10.1007/s00521-020-04877-9

<LI>Mads Valentin Bram, Jan-Peter Calliess, Stephen Roberts, Dennis Hansen, Zhenyu Yang (2020). <BR>
Analysis and Modeling of State-Dependent Delay in Control Valves.<BR>
21st IFAC World Congress (to appear).

<LI>Matthew Willetts, Alexander Camuto, Tom Rainforth, Stephen Roberts, Chris Holmes (2020).<BR>
Improving VAEs' Robustness to Adversarial Attack.<BR>
https://arxiv.org/abs/1906.00230

<LI>Alexander Camuto, Matthew Willetts, Brooks Paige, Chris Holmes and Stephen Roberts (2020).<BR>
Learning Bijective Feature Maps for Linear ICA.<BR>
https://arxiv.org/pdf/2002.07766.pdf

<LI>Philip Ball, Jack Parker-Holder, Aldo Pacchiano,Krzysztof Choromanski and Stephen Roberts (2020).<BR>
Ready Policy One: World Building Through Active Learning.<BR>
http://arxiv.org/abs/2002.02693

<LI>Jack Parker-Holder and Vu Nguyen and Stephen Roberts (2020). <BR>
One-Shot Bayes Opt with Probabilistic Population Based Training.<BR>
http://arxiv.org/abs/2002.02518

<LI>Jack Parker-Holder and Aldo Pacchiano and Krzysztof Choromanski and
Stephen Roberts (2020). <BR>
Effective Diversity in Population-Based Reinforcement Learning. http://arxiv.org/abs/2002.00632

<LI>Bernardo Perez Orozco and Stephen Roberts (2020). <BR>
Zero-shot and few-shot time series forecasting with
ordinal regression recurrent neural networks.<BR>
Proceedings of ESANN 2020 (to appear).

<LI>Bingqing Liu, Ivan Kiskin and Stephen Roberts (2020).<BR>
An Overview of Gaussian process Regression for Volatility Forecasting.<BR>
Proceedings of ICAIIC 2020 (to appear)

<LI>Amir Amel-Zadeh, Jan-Peter Calliess, Daniel Kaiser and Stephen Roberts (2020). <BR>
Machine Learning-Based Financial Statement Analysis.<BR>
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3520684

<LI>Ivan Kiskin, Adam Cobb, Lawrence Wang and Stephen Roberts (2020). <BR>
Humbug Zooniverse: a crowd-sourced acoustic mosquito dataset.<BR>
ICASSP 2020 (to appear)

<LI>Shuyu Lin, Ronald Clark, Robert Birke, Sandro Schoenborn, Niki Trigoni, Stephen Roberts (2020). <BR>
Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model.<BR>
ICASSP 2020 (to appear)

<LI>Z. Zhang, S. Zohren & S. Roberts (2020).<BR>
Deep Reinforcement Learning for Trading.<BR>
The Journal of Financial Data Science, Vol 2, Issue 2.

<LI>Diego Granziol, Xingchen Wan, Timur Garipov, Dmitry Vetrov and Stephen Roberts (2020).<BR>
MLRG Deep Curvature. arXiv:1912.09656

<LI>Arno Blaas, Andrea Patane, Luca Laurenti, Luca Cardelli, Marta Kwiatkowska and Stephen Roberts (2020).<BR>
Adversarial Robustness Guarantees for Classification with Gaussian Processes.<BR>
AISTATS 2020 (to appear)
</ul>

<H3><a NAME="tag2019">2019</a></H3>
<ul>

<LI>Peter Hatfield, Steven Rose, Robbie Scott, Ibrahim Almosallam, Stephen Roberts & Matt Jarvis (2019).<BR>
Using Sparse Gaussian Processes for Predicting Robust Inertial Confinement Fusion Implosion Yields. <BR>
IEEE Transactions on Plasma Physics. https://ieeexplore.ieee.org/document/8875001?source=authoralert

<LI>Anup Aprem, Stephen J. Roberts (2019).<BR>
A Bayesian optimization approach to compute the Nash equilibria of potential games using bandit feedback.<BR>
The Computer Journal (OUP).

<LI>M. Willets, A. Camuto, C. Holmes and S. Roberts (2019).<BR>
Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders.<BR>
NeurIPS workshop on Bayesian Deep Learning.

<LI>Jack Fitzsimons, Sebastian Schmon and Stephen Roberts (2019).<BR>
Implicit Priors for Knowledge Sharing in Bayesian Neural Networks.<BR>
NeurIPS workshop on Bayesian Deep Learning.

<LI>Adam D Cobb, Atilim Günes Baydin, Ivan Kiskin, Andrew Markham and Stephen Roberts (2019).<BR>
Semi-separable Hamiltonian Monte Carlo for inference in Bayesian neural networks.<BR>
NeurIPS workshop on Bayesian Deep Learning.

<LI>D. Granziol, X. Wan, S. Zohren and S. Roberts (2019).<BR>
How does mini-batching affect Curvature information for second order deep learning optimization? <BR>
NeurIPS workshop on Beyond First Order Methods in Machine Learning.
(https://xingchenwan.github.io/papers/Batch_Second_Order_Methods.pdf)

<LI>I. Kiskin, S. Roberts et al. (2019).<BR>
HumBug Zooniverse: a crowdsourced acoustic mosquito dataset.<BR>
NeurIPS 2019 Workshop on Machine Learning for the Developing World.

<LI>K. Polymenakos, S. Roberts et al. (2019). <BR>
Safety Guarantees for Planning Based on Iterative Gaussian Processes.<BR>
NeurIPS 2019 workshop on Safety and Robustness in Decision Making.

<LI>Samuel Kessler, Vu Nguyen, Stefan Zohren and Stephen Roberts (2019). <BR>Indian Buffet Neural Networks for Continual Learning. <BR>
NeurIPS workshop on Bayesian Deep Learning.

<LI>Diego Granziol, Ru Binxin, Stefan Zohren, Xiaowen Dong, Michael Osborne, Steve Roberts (2019).<BR>
Entropic Graph Spectrum. <BR>
NeurIPS 2019 workshop on Information Theory and Machine Learning.

<LI>Xingchen Wan, Diego Granziol, Stefan Zohren, Steve Roberts (2019).<BR>
Closing the K-FAC Generalisation Gap Using Stochastic Weight Averaging. <BR>
NeurIPS 2019 workshop on Beyond First Order Methods in Machine Learning.

<LI>Binxin Ru, Ahsan Alvi, Vu Nguyen, Michael Osborne, Stephen Roberts (2019).<BR>
Bayesian Optimisation over Multiple Continuous and Categorical Inputs. <BR>
NeurIPS workshop on Bayesian Optimisation.

<LI>A. Aprem and S. Roberts (2019). <BR>
Optimal Pricing In Black Box Producer-consumer Stackelberg Games Using Revealed Preference Feedback.
2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP).

<LI> Samuel N. Cohen, Martin Tegnér and Johannes Wiesel (2019).<BR>
Bounding quantiles of Wasserstein distance between true and empirical measure.
https://arxiv.org/abs/1907.02006

<LI> Samuel N. Cohen and Martin Tegnér (2019).<BR>
European Option Pricing with Stochastic Volatility Models Under Parameter Uncertainty.
Frontiers in Stochastic Analysis–BSDEs, SPDEs and their Applications.
https://link.springer.com/chapter/10.1007/978-3-030-22285-7_5
https://arxiv.org/abs/1807.03882

<LI>Anup Aprem & Stephen Roberts (2019).<BR>
Optimal Pricing In Black Box Producer-consumer Stackelberg Games Using Revealed Preference Feedback. IEEE Conference on Machine Learning for Signal Processing (to appear).

<LI>Zhikuan Zhao, Jack K. Fitzsimons, Michael A. Osborne, Stephen J. Roberts, and Joseph F. Fitzsimons (2019).<BR>
Quantum algorithms for training Gaussian processes.
Phys. Rev. A 100, 012304 – Published 8 July 2019.
URL: https://link.aps.org/doi/10.1103/PhysRevA.100.012304.
DOI: 10.1103/PhysRevA.100.012304

<LI>Arnold Salas, Samuel Kessler, Stefan Zohren, Stephen Roberts (2019).<BR>
Practical Bayesian Learning of Neural Networks via Adaptive Subgradient Methods.
arXiv 1811.03679

<LI>Diego Granziol, Binxin Ru, Stefan Zohren, Xiaowen Dong, Michael Osborne and Stephen Roberts (2019).<BR>
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning.
Entropy 2019, 21(6), 551; doi:10.3390/e21060551
arXiv 1906.01101

<LI>Bryan Lim, Stefan Zohren and Stephen Roberts (2019).<BR>
Enhancing Time Series Momentum Strategies Using Deep Neural Networks.
The Journal of Financial Data Science.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3369195.
http://arxiv.org/abs/1904.04912

<LI>Binxin Ru, Ahsan S. Alvi, Vu Nguyen, Michael A. Osborne, Stephen J Roberts (2019).<BR>
Bayesian Optimisation over Multiple Continuous and Categorical Inputs.
https://arxiv.org/abs/1906.08878

<LI>Stefan Zohren and Stephen Roberts (2019).<BR>
Gradient descent in Gaussian random fields as a toy model for high-dimensional optimisation.
ICML workshop on Physics for Deep Learning.
https://sites.google.com/view/icml2019phys4dl/accepted-papers

<LI>Arnold Salas, Samuel Kessler, Stefan Zohren, Stephen Roberts (2019).<BR>
Practical Bayesian Learning of Neural Networks via Adaptive Subgradient Methods.
ICML 2019 Workshop on Theoretical Physics for Deep Learning.
https://arxiv.org/abs/1811.03679

<LI>Diego Granziol, Stefan Zohren, Stephen Roberts, Dmitry P Vetrov, Andrew Gordon Wilson, Timur Garipov (2019).<BR>
The Deep Learning Limit: Negative Neural Network eigenvalues just noise?
ICML workshop on Physics for Deep Learning.
https://sites.google.com/view/icml2019phys4dl/accepted-papers

<LI>Bryan Lim, Stefan Zohren, Stephen Roberts (2019).<BR>
Title: Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs.
ICML 2019 Time Series Workshop.
http://arxiv.org/abs/1905.09691

<LI>Ivan Kiskin, Udeepa Meepegama and Stephen Roberts (2019).<BR>
Super-resolution of Time-series Labels for Bootstrapped Event Detection.
ICML 2019 Time Series Workshop.

<LI>Zihao Zhang, Stefan Zohren and Stephen Roberts (2019).<BR>
Extending Deep Learning Models for Limit Order Books to Quantile Regression.
ICML 2019 Time Series Workshop.

<LI>Jaleh Zand and Stephen Roberts (2019).<BR>
Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN).
ICML 2019 Time Series Workshop.

<LI>Shuyu Lin, Ronald Clark, Robert Birke, Niki Trigoni and Stephen Roberts (2019).<BR>
WiSE-ALE: Wide Sample Estimator for Aggregate Latent Embedding.
DeepGenStruct 2019.
http://arxiv.org/abs/1902.0616

<LI>Zihao Zhang, Stefan Zohren, Stephen Roberts (2019).<BR>
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
IEEE Transactions on Signal Processing, Volume: 67, Issue:11. Page(s): 3001-3012

<LI>Ahsan Alvi, Binxin Ru, Jan-Peter Calliess, Stephen Roberts, Michael A. Osborne (2019).<BR>
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation.
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:253-262, 2019.
http://proceedings.mlr.press/v97/alvi19a.html

<LI>Edwin Simpson, Steven Reece, Stephen J. Roberts (2019).<BR>
Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources.
https://arxiv.org/abs/1904.03063

<LI>Kyriakos Polymenakos, Alessandro Abate, Stephen Roberts (2019).<BR>
Safe Policy Search Using Gaussian Process Models.
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems.
Pages 1565-1573.

<LI>Daniel Poh, Stephen Roberts, Martin Tegner (2019).<BR>
A Machine Learning approach to Risk Minimisation in Electricity Markets
with Coregionalized Sparse Gaussian Processes.
http://arxiv.org/abs/1903.09536

<LI>Timos Papadopoulos, Stephen J. Roberts and Katherine J. Willis (2019).<BR>
Automated bird sound recognition in realistic settings.
https://arxiv.org/pdf/1809.01133.pdf

<LI>Jack Fitzsimons, AbdulRahman Al Ali, Michael Osborne and Stephen Roberts (2019).<BR>
A General Framework for Fair Regression.
http://arxiv.org/abs/1810.05041

<LI>Bryan Lim, Stefan Zohren, Stephen Roberts (2019).<BR>
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction.
http://arxiv.org/abs/1901.08096

<LI>Martin Tegner and Stephen Roberts (2019).<BR>
A Probabilistic Approach to Nonparametric Local Volatility.
http://arxiv.org/abs/1901.06021

<LI>Matthew Willetts, Aiden Doherty, Stephen J. Roberts and Christopher C. Holmes (2019).<BR>
Semi-unsupervised Learning using Deep Generative Models.
http://arxiv.org/abs/1810.12176

<LI>Zihao Zhang, Stefan Zohren, Stephen Roberts (2019).<BR>
BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books.
http://arxiv.org/abs/1811.10041

<LI>Anup Aprem, Stephen J. Roberts (2019).<BR>
Title: A Bayesian optimization approach to compute the Nash equilibria of potential games using bandit feedback.
http://arxiv.org/abs/1811.06503

<LI>Arnold Salas, Stefan Zohren, Stephen Roberts (2019).<BR>
Practical Bayesian Learning of Neural Networks via Adaptive Subgradient Methods.
http://arxiv.org/abs/1811.03679

<LI>Richard Everett, Adam Cobb, Andrew Markham, Stephen Roberts (2019).<BR>
Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems.
Pages 1943-1945

<LI>Jack Fitzsimons, AbdulRahman Al Ali, Michael Osborne and Stephen Roberts (2019).<BR>
Equality Constrained Decision Trees: For the Algorithmic Enforcement of Group Fairness.
http://arxiv.org/abs/1810.05041

</ul>

<H3><a NAME="tag2018">2018</a></H3>
<ul>

<LI>O Isupova, Y Li, D Kuzin, SJ Roberts, K Willis, S Reece (2018).<BR>
BCCNet: Bayesian classifier combination neural network.
<i>arXiv preprint</i> arXiv:1811.1225

<LI>Jack Fitzsimons, Michael Osborne and Stephen Roberts (2018).<BR>
Intersectionality: Multiple Group Fairness in Expectation Constraints.
NIPS Workshop on Ethical, Social and Governance Issues in AI

<LI>M. Willetts, C. Holmes and S. Roberts (2018).<BR>
Semi-unsupervised Learning of Human Activity using Deep Generative Models
NIPS Machine Learning for Health Workshop.

<LI>Wolfgang Fruehwirt, Adam Cobb, Stephen Roberts and Georg Dorffner (2018).<BR>
Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer’s disease severity.
NIPS Machine Learning for Health Workshop.

<LI>Bernardo Pérez Orozco, Gabriele Abbati, Stephen Roberts (2018).<BR>
MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting. ArXiv 1803.09704

<LI>Zihao Zhang, Stefan Zohren, Stephen Roberts (2018).<BR>
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books. arXiv 1808.03668

<LI> Jan Calliess, A. Papachristodoulou, Stephen Roberts (2018). <BR>
Bayesian nonparametrics and feedback-linearisation of discretised control-affine systems. CDC, 2018.

<LI> Martin Tegnér, Benjamin Bloem-Reddy, Stephen Roberts (2018). <BR>
Sequential sampling of Gaussian latent variable models.
ICML 2018 and http://arxiv.org/abs/1807.04932

<LI> Mark McLeod, Michael Osborne and Stephen Roberts (2018). <BR>
Adaptive Quadrature for Fast Sequential Hyperparameter Marginalization of Gaussian Processes. ICML 2018.

<li>Ivan Kiskin, Davide Zilli, Yunpeng Li, Marianne Sinka, Kathy Willis and Stephen Roberts (2018).<BR>
<a href="https://rdcu.be/3Rpo">Bioacoustic detection with wavelet-conditioned convolutional neural networks</a>.<BR>
Neural Computing and Applications. Online journal version available <a href="http://link.springer.com/article/10.1007/s00521-018-3626-7">here</a>.

<li>Min Yang, Charlie Fehl, Karen V. Lees, Eng-Kiat Lim, Wendy Offen, Gideon J. Davies, Dianna J. Bowles, Stephen J. Roberts, and Benjamin G. Davis. (2018).<BR>Functional and informatics analysis enables glycosyltransferase activity prediction.<BR>
Nature Chemical Biology (to appear).

<LI>J.K. Fitzsimons, M. Osborne, S. Roberts and J.F. Fitzsimons (2018). <BR>Improved stochastic trace estimation using mutually unbiased bases. <BR>UAI 2018 and arXiv https://arxiv.org/abs/1608.00117

<LI>M. McLeod, M. Osborne, S. Roberts (2018). <BR>Optimization, fast and slow: optimally switching between local and Bayesian optimization. <BR>ICML 2018. Also https://arxiv.org/abs/1805.08610

<LI>Mark McLeod, Michael A. Osborne and Stephen J Roberts (2018). <BR>Adaptive Quadrature for Fast Sequential Hyperparameter Marginalization. <BR>ICML workshop on autoML.

<LI>Martin Tegner, Ben Bloem-Reddy and Stephen Roberts (2018).<BR>
Sequential sampling of Gaussian process latent variable models.<BR>
ICML workshop on Tractable Probabilistic Models.

<LI>S. Ghoshal & S. Roberts (2018). <BR>Thresholded ConvNet Ensembles: Neural Networks for Technical Forecasting. <BR>Data Science in FinTech, KDD 2018.

<LI>Arno Blaas, Adam Cobb, Jan Calliess and Stephen Roberts (2018). <BR>Scalable Bounding of Predictive Uncertainty in Regression Problems with SLAC. <BR>Proceedings of SUM 2018.

<LI>Adam D. Cobb, Stephen J. Roberts, Yarin Gal (2018). <BR>Loss-Calibrated Approximate Inference in Bayesian Neural Networks. <BR>ICML Theory of Deep Learning workshop. ArXiv http://arxiv.org/abs/1805.03901

<LI>B. Damghani, S. Roberts (2018). <BR>Machine Learning Techniques for Deciphering Implied Volatility Surface Data in a Hostile Environment: Scenario Based Particle Filter, Risk Factor Decomposition & Arbitrage Constraint Sampling. <BR>SSRN https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3133862

<LI>Diego Granziol, Binxin Ru, Stefan Zohren, Xiaowen Dong, Michael Osborne, Stephen Roberts (2018). <BR>Entropic Spectral Learning in Large Scale Networks. <BR>arXiv https://arxiv.org/abs/1804.06802

<LI>Zhikuan Zhao, Jack K. Fitzsimons, Michael A. Osborne, Stephen J. Roberts and Joseph F. Fitzsimons (2018). <BR>Quantum algorithms for training Gaussian Processes. <BR>arXiv http://arxiv.org/abs/1803.10520

<BR>Bernardo Perez Orozco, Gabriele Abbati, Stephen Roberts (2018). <BR>MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting. <BR>ArXiv http://arxiv.org/abs/1803.09704

<LI>Jan-Peter Calliess, Stephen Roberts, Carl Edward Rasmussen, Jan M. Maciejowski (2018). <BR>Nonlinear Set Membership Regression with Adaptive Hyper-Parameter Estimation for Online Learning and Control. <BR> European Control Conference 2018.

<li>Adam D. Cobb, Richard Everett, Andrew Markham, Stephen J. Roberts (2018).<BR>
<a href="https://arxiv.org/abs/1802.10446">Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus.</a><BR>
KDD 2018. ACM Proceedings. Also arXiv 1802.10446<BR>

<li>Trung Dong Huynh, Mark Ebden, Joel Fischer, Stephen Roberts, Luc Moreau (2018).<BR>
<a href="http://link.springer.com/article/10.1007/s10618-017-0549-3">Provenance Network Analytics: An approach to data analytics using data provenance.</a><BR>
Data Mining and Knowledge Discovery, DOI 10.1007/s10618-017-0549-3<BR>

<li>Richard Everett and Stephen Roberts (2018).<BR>
Opponent Modelling of Non-Stationary Agents with Deep Reinforcement Learning.<BR>
Learning, Inference and Control of Multi-Agent Systems. AAAI 2018

<li>Oliver Padget, Sarah L. Bond, Marwa M. Kavelaars, Emiel van Loon, Mark Bolton, Annette L. Fayet, Martyna Syposz, Stephen Roberts and Tim Guilford (2018).<BR>
In-situ clock-shift reveals that the sun-compass contributes to orientation in a wild pelagic seabird.<BR>
Current Biology.
</ul>

<H3><a NAME="tag2017">2017</a></H3>
<ul>

<li>Wolfgang Fruehwirt, Matthias Gerstgrasser, Pengfei Zhang, Leonard Weydemann, Stephen Roberts and Georg Dorffner (2017).<BR>
Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer’s disease. <BR>
NIPS workshop: ML4H 2017<BR>
https://arxiv.org/pdf/1711.08359

<li>Adam D. Cobb, Andrew Markham, Stephen J. Roberts (2017).<BR>
Learning from lions: inferring the utility of agents from their trajectories.<BR>
https://arxiv.org/abs/1709.02357

<li>D. Hendricks, A. Cobb, R. Everett, J. Downing and S.J. Roberts (2017).<BR>
Inferring agent objectives at different scales of a complex adaptive system.<BR>
NIPS workshop: Learning in the Presence of Strategic Behavior.<BR>
https://arxiv.org/abs/1712.01137<BR>
https://www.cs.cmu.edu/~nhaghtal/mlstrat/papers/everettshort.pdf

<li>Kyriakos Polymenakos, Alessandro Abate, Stephen Roberts (2017).<BR>
Safe Policy Search with Gaussian Process Models.<BR>
NIPS workshop: Transparent and interpretable machine learning in safety critical environments.<BR>
https://arxiv.org/abs/1712.05556

<li>Jaleh Zand and Stephen Roberts (2017).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/nips_2017.pdf">MiDGaP: Mixture Density Gaussian Processes.</a><BR>
NIPS 2017 timeseries workshop.

<li>O. Bent, S. Remy, S. Roberts and A. Walcott-Bryant (2018).<BR>
Deploying Novel Exploration Techniques (NETs) for Malaria Policy Interventions<BR>
Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-18)<BR>
https://arxiv.org/pdf/1712.00428.pdf

<li>W. Fruehwirt, P. Zhang, M. Gerstgrasser, M. Osborne, D. Grossegger, R. Schmidt, T. Benke, P. Dal-Bianco, G. Ransmayr, L. Weydemann, H. Garn, M. Waser, S. Roberts and G. Dorffner (2017).<BR>
Gaussian process classification from multivariate spatio-temporal brain potential patterns in Alzheimer’s disease.<BR>
IJCAI-BOOM 2017.

<li>Glen Wright Colopy, Stephen J. Roberts, and David A. Clifton (2018).<BR>
Bayesian Optimization of Personalized Models for Patient Vital-Sign Monitoring.<BR>
IEEE Journal Of Biomedical And Health Informatics.

<li>S. Ghoshal & S. Roberts (2017).<BR>
Reading the Tea Leaves: A Neural Network Perspective on Technical Trading.<BR>
Proceedings of KDD 2017.

<li>B. Mahdavi-Damghani, K. Mustafayeva, S. Roberts and C. Buescu (2017).<BR>
Portfolio Optimization in the Context of Cointelated Pairs: Stochastic Differential Equation vs. Machine Learning Approach.<BR>
SSRN https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3039171

<li>B. Mahdavi-Damghani and S. Roberts (2017).<BR>
Deciphering Price Formation In The High Frequency Domain: Systems & Evolutionary Dynamics As Keys For Construction Of The High Frequency Trading Ecosystem.<BR>SSRN https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3039188

<li>B. Mahdavi-Damghani and S. Roberts (2017).<BR>
A Proposed Risk Modeling Shift From The Approach Of Stochastic Differential Equation Towards Machine Learning Clustering: Illustration With The Concepts Of Anticipative & Responsible VaR.<BR> SSRN https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3039179

<li>B. Mahdavi-Damghani and S. Roberts (2017).<BR>
A Proposed Risk Modeling Shift from the Approach of Stochastic Differential Equation towards Machine Learning Clustering: Illustration with the Concepts of Anticipative & Responsible VaR.<BR> SSRN https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3039179

<li>B. Mahdavi-Damghani, K. Mustafayeva, S. Roberts and C. Buescu (2017).<BR>
Convergence of Heston to SVI Proposed Extensions: Rational & Conjecture for the Convergence of Extended Heston to the Implied Volatility Surface Parametrization.<BR>
SSRN https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3039185

<li>Y. Li, D. Zilli, H. Chan, I. Kiskin, M. Sinka, and S. Roberts (2017).<BR>
Mosquito detection with low-cost smartphones: data acquisition for malaria research.<BR> NIPS Workshop on Machine Learning for the Developing World, Long Beach, USA, Dec. 2017. Also arXiv https://arxiv.org/abs/1711.06346

<li>Y. Li, I. Kiskin, M. Sinka, H. Chan, and S. Roberts (2017).<BR>
Cost-sensitive detection with variational autoencoders for environmental acoustic sensing.<BR> NIPS Workshop on Machine Learning for Audio Signal Processing, Long Beach, USA, Dec. 2017.

<li>I. Kiskin, B. Orozco, T. Windebank, D. Zilli, M. Sinka, K. Willis, S. Roberts (2017).<BR>
Mosquito Detection with Neural Networks: The Buzz of Deep Learning. arXiv.

<li>Z. Gomes, M. Jarvis, I. Almosallam and S. Roberts (2017).<BR>
Improving Photometric Redshift Estimation using GPz: size information, post processing and improved photometry.<BR> Monthly Notices of the Royal Astronomical Society (to appear). Also arXiv http://arxiv.org/abs/1712.02256

<li>D. Granziol and S. Roberts (2017).<BR>
Entropic Determinants (2017).<BR>
Proceedings of IEEE Big Data 2017

<li>D. Granziol and S. Roberts (2017).<BR>
Entropic Determinants of Massive Matrices.<BR>
arXiv https://arxiv.org/pdf/1709.02702.pdf

<LI>S. Aigrain, H. Parviainen, S. Roberts, S. Reece and T. Evans (2017).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/arc2_2017.pdf">Robust, open-source removal of systematics in Kepler data.</a><BR>
Monthly Notices of the Royal Astronomical Society.<BR>

<LI>E. Simpson, S. Reece and S. Roberts (2017).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/heatmaps_ibcc_ecml_2017.pdf">Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources.</a><BR>
Proceedings of ECML 2017.<BR>

<LI>G. Calopy, T. Zhu, L. Clifton, S. Roberts and D. Clifton (2017).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/embc2017.pdf">Likelihood-based artefact detection in continuously-acquired patient vital signs.</a><BR>
To appear in Proceedings of EMBC 2017.<BR>

<LI>D. Hendricks and S. Roberts (2017).<BR>
<a href="https://arxiv.org/abs/1704.08488">Optimal client recommendation for market makers in illiquid financial products.</a><BR>
ArXiv 1704.08488. To appear in proceedings of ECML 2017.<BR>

<LI>S. A. Rizvi, S. Roberts, M. Osborne and F.Nyikosa (2017).<BR>
<a href="https://arxiv.org/abs/1705.00891">A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes.</a><BR>
ArXiv 1705.00891.<BR>

<LI>J. Fitzsimons, D. Granziol, K. Cutajar, M. Osborne, M. Filippone and S. Roberts (2017).<BR>
<a href="https://arxiv.org/abs/1704.07223">Entropic Trace Estimates for Log Determinants.</a><BR>
ArXiv 1704.07223. To appear in proceedings of ECML 2017.<BR>

<LI>D. Granziol and S. Roberts (2017).<BR>
<a href="https://arxiv.org/pdf/1703.10099.pdf">An information and field theoretic approach to the grand canonical ensemble.</a><BR>
ArXiv 1703.10099.<BR>

<LI>J. Fitzsimons, K. Cutajar, M. Osborne, S. Roberts and M. Filippone (2017).<BR>
<a href="https://arxiv.org/pdf/1704.01445.pdf">Bayesian Inference of Log Determinants.</a><BR>
ArXiv 1704.01445 and Proceedings of UAI 2017.<BR>

<LI>M. McLeod, M. Osborne and S. Roberts (2017).<BR>
<a href="https://arxiv.org/pdf/1703.04335.pdf">Practical Bayesian Optimization for Variable Cost Objectives.</a><BR>
ArXiv 1703.04335.<BR>

<LI>J. Bewsher, A. Tosi, M. Osborne and S. Roberts (2017).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/Bewsher_aistats2017.pdf">Distribution of Gaussian Process Arc Lengths.</a><BR>
Proceedings of AISTATS 2017. Also available from arXiv http://arxiv.org/abs/1703.08031<BR>

<LI>G. Colopy, M. Pimental, S. Roberts, D. Clifton (2017).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/bhi_healthcare_gp_2017.pdf">Bayesian Optimisation of Gaussian Processes for Identifying the Deteriorating Patient.</a><BR>
Proceedings of BHI 2017.<BR>

<LI>S. Rizvi, E. van Heerden, A. Salas, F. Nyikosa, S. Roberts, M. Osborne and E. Rodriguez (2017).<BR>
Identifying Sources of Discrimination Risk in the Life Cycle of Machine Intelligence Applications under New European Union Regulations.<BR>
To appear in AAAI 2017 Spring Symposium on Artificial Intelligence for Social Good, Stanford, March 2017.<BR>

</ul>

<H3><a NAME="tag2016">2016</a></H3>
<ul>

<LI>Clarke, P.E., Coveney, P., Heavens, A.F., Jäykkä, J., Joachimi,
B., Karastergiou, A., Konstantinidis, N., Korn, A., Mann, R., McEwen, J.D., Ridder, S., Roberts, S., Scanlon, T.,
Shellard, E.P., & Yates, J.N. (2016).<BR>
<a href="https://indico.cern.ch/event/449964/attachments/1253648/1849677/Bigdata_physicalsciences.pdf">Big
Data in the physical sciences: challenges and opportunities.</a>

<LI>E. van Heerden, A. Karastergiou and S. Roberts (2016).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/pulsarPipeline2016.pdf">A Framework for Assessing the Performance of Pulsar Search Pipelines.</a><BR>
Monthly Notices of the Royal Astronomical Society 2016. doi: 10.1093/mnras/stw3068<BR>

<LI>S. Ramchurn, S. Reece, E. Simpson, T. Huynh, T. Rodden, S. Roberts, L. Moreau and N. Jennings (2016)<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/jair_disaster_response_2017.pdf">A Disaster Response System based on Human-Agent Collectives.</a><BR>
Journal of AI Research, 57 (2016) 661-708.<BR>

<LI>Y.-L, Kom Samo , S. Roberts. (2016).<BR>
<a href="http://www.jmlr.org/papers/volume17/15-382/15-382.pdf">String and Membrane Gaussian Processes.</a><BR>
<I>Journal of Machine Learning Research.</I> Vol 17, 2016.

<LI>J.K. Fitzsimons, M. Osborne, S. Roberts and J.F. Fitzsimons (12016).<BR>
<a href="http://arxiv.org/abs/1608.00117">Improved stochastic trace estimation using mutually unbiased bases.</a><BR>
ArXiv 1608.00117.<BR>

<LI>I. Almosallam, M. Jarvis and S. Roberts (2016).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/photogz2016.pdf ">GPz: Non-stationary sparse Gaussian processes for heteroscedastic uncertainty estimation in photometric redshift.</a><BR>Monthly Notices of the Royal Astronomical Society 2016.
doi: 10.1093/mnras/stw1618<BR>

<LI>C. Lloyd, T. Gunter, M. Osborne, S. Roberts and T. Nickson (2016).<BR>
<a href="http://jmlr.org/proceedings/papers/v51/lloyd16.html">Latent Point Process Allocation.</a><BR>
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, pp. 389–397, 2016<BR>

<LI>G. Calopy, M. Pimental, S. Roberts and D. Clifton (2016).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/EMBC2016.pdf">Bayesian Gaussian Processes for Identifying the Deteriorating Patient.</a><BR>
To appear in <i>Proceedings of EMBC 2016.</i>

<LI>S. Ghoshal and S. Roberts (2016).<BR>
<a href="http://arxiv.org/abs/1603.06202">Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes.</a><BR>
Algorithmic Finance, vol. 5, no. 1-2, pp. 21-30, 2016. DOI: 10.3233/AF-160055.<BR>

<LI>N. Chancellor, S. Zohren, P. Warburton, S. Benjamin and S. Roberts (2016).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/nature_ksat_quant_comp_2016.pdf">
A Direct Mapping of Max k-SAT and High Order Parity Checks to a Chimera Graph.</a><BR>
<i>Nature.</i> 6:37107 \| DOI: 10.1038/srep37107<BR>

<LI>P. Brook, A. Karastergiou, S. Johnston, M. Kerr, R. Shannon and S. Roberts (2016).<BR>
<a href="http://mnras.oxfordjournals.org/cgi/reprint/stv2715?ijkey=3H8mcbvqTsHPu7y&keytype=ref">
Emission-rotation correlation in pulsars: new discoveries with optimal techniques.</a><BR>
Monthly Notices of the Royal Astronomical Society 2016 456 (2): 1374-1393
doi: 10.1093/mnras/stv2715 <BR>
</ul>

<H3><a NAME="tag2015">2015</a></H3>
<ul>

<LI>I. Almosallam, S. Lindsay, M. Jarvis and S. Roberts (2015).<BR>
<a href="http://mnras.oxfordjournals.org/content/455/3/2387.full.pdf?keytype=ref&ijkey=4C6LdcW2jvJoPcz">
A Sparse Gaussian Process Framework for Photometric Redshift Estimation.</a><BR>
Monthly Notices of the Royal Astronomical Society 2015 455 (3): 2387-2401
doi: 10.1093/mnras/stv2425.<BR>

<LI>F. Nyikosa, M. Osborne and S. Roberts (2015).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/NyikosaOsborneRobertsNipsBayesopt2015.pdf">Adaptive Bayesian Optimisation for Online Portfolio Selection.</a><BR>
In <i>Workshop on Bayesian Optimization at NIPS 2015</i>.<BR>

<LI>V. Rajpaul, S. Aigrain and S. Roberts (2015).<BR>
<a href="http://arxiv.org/pdf/1510.05598v1.pdf">Ghost in the time series: no planet for Alpha Cen B.</a><BR>
Monthly Notices of the Royal Astronomical Society. 456 (1), L6-L10.<BR>

<LI>Y.-L. Kom Samo, S. Roberts (2015).<BR>
<a href="http://arxiv.org/abs/1510.02830">p-Markov Gaussian Processes for Scalable and Expressive Online Bayesian Nonparametric Time Series Forecasting.</a>
<BR>Under review<BR>

<LI>Y.-L. Kom Samo, S. Roberts (2015).<BR>
<a href="http://arxiv.org/abs/1506.02236">Generalized Spectral Kernels.</a>
<BR>Technical Report<BR>

<LI>Y.-L. Kom Samo, S. Roberts (2015).<BR>
<a href="http://arxiv.org/abs/1506.02239">String Gaussian Process Kernels.</a>
<BR>Technical Report<BR>

<LI>T. Papadopoulos, S. Roberts and K. Willis (2015).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/ermites_arxiv_timos.pdf">Detecting bird sound in unknown acoustic background using crowdsourced training data.</a><BR>
ArXiv Stat.ML/1251827.<BR>

<LI> Briol, F.-X., Oates, C. J., Girolami, M., & Osborne, M. A. (2015).<BR><a href="http://arxiv.org/abs/1506.02681">Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees.</a><BR> Advances In Neural Information Processing Systems (NIPS) 2015.<BR>

<LI>Hennig, P., Osborne, M. A., & Girolami, M. A. (2015).<BR><a href="http://rspa.royalsocietypublishing.org/content/royprsa/471/2179/20150142.full.pdf?ijkey=wr6Ggr6GGGgbJYr&keytype=ref">Probabilistic Numerics and Uncertainty in Computations.</a><BR> Proceedings Of the Royal Society A.<BR>

<LI>A. Salas, S.J. Roberts, M.A. Osborne (2015).<BR>
<a href="http://arxiv.org/abs/1509.02438">
A Variational Bayesian State-Space Approach to Online Passive-Aggressive Regression.</a><BR>
ArXiv 1509.02438<BR>

<LI>V. Rajpaul, S. Aigrain, M. Osborne, S. Reece and S. Roberts (2015).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/GP_framework_paper.pdf">
A Gaussian process framework for modelling stellar activity signals in radial velocity data.</a><BR>
Monthly Notices of the Royal Astronomical Society 2015.<BR>

<LI>A. Karastergsiou et al. (2015).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/artemis_paper_r1.pdf">
Limits on Fast Radio Bursts at 145 MHz with ARTEMIS, a real-time software backend.</a><BR>
Monthly Notices of the Royal Astronomical Society 2015.<BR>

<LI>C. Lloyd, T. Gunter, M. Osborne and S. Roberts. (2015).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/lloyd15vbgpmpp.pdf">
Variational Inference for Gaussian Process Modulated Poisson Processes.</a><BR>
Proceedings of ICML 2015. pp. 389-397. http://jmlr.org/proceedings/papers/v51/lloyd16.html<BR>

<LI>Y-L. Kom Samo and S. Roberts. (2015).<BR>
<a href="http://jmlr.org/proceedings/papers/v37/samo15.pdf">
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes.</a><BR>
Proceedings of ICML 2015.<BR>

<LI>I. Almosallam, S. Lindsay, M. Jarvis and S. Roberts. (2015).<BR>
<a href="http://arxiv.org/pdf/1505.05489v1.pdf">
A Sparse Gaussian Process Framework for Photometric Redshift Estimation.</a><BR>
ArXiv 1505.05489<BR>

<LI>S. Ramchurn, T.D. Huynh, Y. Ikuno, J. Flann, F. Wu, L. Moreau, N. Jennings, J. Fischer, W. Jiang, T. Rodden, E. Simpson, S. Reece and S. Ro\
berts (2015).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/fp634-ramchurn.pdf">
HAC-ER: A Disaster Response System based on Human-Agent Collectives"</a><BR>
AAMAS-2015 <B>Best Paper Award</B><BR>

<LI> I. Psorakis et al. (2015).<BR>
<a href="http://link.springer.com/article/10.1007/s00265-015-1906-0">
Inferring social structure from temporal data.</a><BR>
Behavioral Ecology and Sociobiology.<BR>

<li>E. Simpson, M. Venanzi, P. Kohli, J. Guiver, S. Reece, S. Roberts, N. Jennings (2015).<BR>
<a href="http://www.robots.ox.ac.uk/~edwin/WWW15.pdf">Language Understanding in the Wild: Combining Crowdsourcing and Machine Learning</a><BR>
Proceedings of the 24th International World Wide Web Conference.<BR>

<LI>E. Simpson, S. Roberts (2015). <br>
<a href="http://www.robots.ox.ac.uk/~edwin/book14.pdf">Bayesian Methods for Intelligent Task Assignment in Crowdsourcing Systems</a><BR>
Scalable Decision Making: Uncertainty, Imperfection, Deliberation and Scalability, Studies in Computational Intelligence, Springer, p. 1-32.<BR>

<li> M. Ebden, T.D. Huynh, L. Moreau, and S. Roberts (2015).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/IncentiveEngineeringAAMAS2015.pdf">
Incentive Engineering through Subgraph Matching - with Application to Task Allocation.</a><BR>
Fourth International Workshop on Human Agent Interaction Design and Models, AAMAS, Istanbul, 4 May 2015.<BR>

<LI>S. Aigrain, S. T. Hodgkin, M. J. Irwin, J. R. Lewis and S. J. Roberts. (2015).<BR>
<a href="http://arxiv.org/abs/1412.6304">Precise time-series photometry for the Kepler-2.0 mission.</a><BR>
Monthly Notices of the Royal Astronomical Society.<BR>

</ul>

<H3><a NAME="tag2014">2014</a></H3>
<ul>
<LI>Y-L. Kom Samo, S. Roberts (2014).<BR>
<a href = "http://arxiv.org/pdf/1410.6834v1">Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes.</a><BR>
arXiv, October 2014.

<LI>Gunter, T., Lloyd, C., Osborne, M. A., & Roberts, S. J. (2014).<BR>
<a href = "http://www.robots.ox.ac.uk/~sjrob/Pubs/gunter_uai_2014_1407.6949v1.pdf">
Efficient Bayesian Nonparametric Modelling of Structured Point Processes.</a><BR>
In Proceedings of Uncertainty in Artificial Intelligence (UAI).

<LI>Gunter, T., Osborne, M. A., Garnett, R., Hennig, P., & Roberts, S. J. (2014).<BR>
<a href = "http://www.robots.ox.ac.uk/~sjrob/Pubs/Gunter2014_samp.pdf">
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature.</a><BR>
Advances in Neural Information Processing Systems (NIPS) 2014.

<LI>N. Jennings, L. Moreau, D. Nicholson, S. Ramchurn, S. Roberts, T. Rodden and A. Rogers (2014).<BR>
<a href = "http://www.robots.ox.ac.uk/~sjrob/Pubs/hac.pdf">Human-Agent Collectives.</a><BR>
Communications of the ACM, vol. 57, N. 12. DOI:10.1145/2629559. <BR>

<LI>T. Nickson, M. Osborne, S. Reece, S. Roberts (2014).<BR>
<a href="http://arxiv.org/pdf/1407.7969v1">
Automated Machine Learning on Big Data using Stochastic Algorithm Tuning.</a><BR>
arXiv:1407.7969 [stat.ML]. <BR>

<LI>S. Reece, S. Roberts, S. Ghosh, A. Rogers, N. Jennings. (2014).<BR>
<a href="http://jmlr.org/papers/volume15/reece14a/reece14a.pdf">Efficient State-Space Inference of
Periodic Latent Force Models.</a><BR>
<I>Journal of Machine Learning Research.</I> vol 15, 2337-2397.

<LI> N. Gillani, R. Eynon, M. Osborne, I. Hjorth and S. Roberts. (2014).<BR>
<a href="http://arxiv.org/pdf/1403.4640v2.pdf">Communication Communities in MOOCs.</a><BR>
arXiv 1403.4640.<BR>

<LI> A. Levenberg, S. Pulman, K. Moilanen, E. Simpson, S. Roberts (2014).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/sentiment_ICICA2014.pdf">
Predicting Economic Indicators from Web Text Using Sentiment Composition.</a><BR>
Proceedings of ICICA-2014. <B>Best paper award</B>

<LI>
J. Calliess, M. Osborne and S.J. Roberts. (2014).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/fp604-calliessA.pdf">
Conservative collision prediction and avoidance for stochastic trajectories in continuous time and state.</a><BR>
International Conference on Autonomous Agents and Multiagent Systems (AAMAS2014)

<li>
T.D. Huynh, M. Ebden, S. Ramchurn, S. Roberts, and L. Moreau (2014).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/DataQuality2014.pdf">
Data Quality Assessment From Provenance Graphs.</a><BR>
Provenance Week 2014, Cologne, Germany, 9-13 June 2014.

<LI>
J. Calliess, M. Osborne and S.J. Roberts. (2014).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/AAMASjms_arxivreport.pdf">
Conservative collision prediction and avoidance for stochastic trajectories in continuous time and state. </a><BR>
<i>Technical report & supplementary material.</i><BR>

</UL>

<H3><a NAME="tag2013">2013</a></H3>
<ul>

<LI>J. Krause, S. Krause, R. Arlinghaus, I. Psorakis, S. Roberts, C. Rutz. (2013).<BR>
Reality mining of animal social systems.<BR>
Trends in Ecology and Evolution. 07/2013; DOI: 10.1016/j.tree.2013.06.002

<LI>
A. Levenberg, E. Simpson, S. Roberts and G. Gottlob (2013).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/solid_final.pdf">
Economic Prediction using Heterogeneous Data Streams from the World Wide Web.</a><BR>
ECML workshop on Scalable Methods in Decision Making.

<LI>
J. Calliess, M. Osborne and S.J. Roberts. (2013).<BR>
Nonlinear adaptive hybrid control by combining Gaussian process system identification with classical control laws.<BR>
Novel Methods for Learning and Optimization of Control Policies and Trajectories for Robotics, ICRA, 2013.

<LI>
J. Calliess, A. Papachristodoulou, S.J. Roberts (2013).<BR>
Stochastic processes and feedback-linearisation for online identification and Bayesian adaptive control of fully-actuated mechanical systems.<BR>
Advances in Machine Learning for Sensorimotor Control, NIPS, 2013. <a href="http://arxiv.org/abs/1311.4468">arXiv (longer) version.

<LI>
P. Brook, A. Karastergiou, S. Buchner, S. Roberts, M. Keith, S. Johnston and R. Shannon(2013).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/apj_revised.pdf">Evidence of an asteroid encountering a pulsar.</a> <BR>
The Astrophysical Journal (to appear).
<a href="http://arxiv.org/abs/1311.3541">ArXiv version.</a>

<LI>
S. Roberts, A. McQuillan, S. Reece and S. Aigrain (2013).<BR>
<a href="http://mnras.oxfordjournals.org/cgi/reprint/stt1555?ijkey=yjwhzmV6OcyLWiX&keytype=ref">Astrophysically robust systematics removal using variational inference: application to the first month of Kepler data.</a> <BR>
Monthly Notices of the Royal Astronomical Society. doi: 10.1093/mnras/stt1555.
<a href="http://arxiv.org/pdf/1308.3644">ArXiv version.</a>

<li>
T.D. Huynh, M. Ebden, M. Venanzi, S. Ramchurn, S. Roberts, L. Moreau (2013).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/AAAI_CHCC_2013.pdf">
Interpretation of Crowdsourced Activities Using Provenance Network Analysis.</a><BR>
AAAI Conference on Human Computation & Crowdsourcing, Palm Springs, California, 7-9 November 2013.<BR>

<LI>
N. Shah and S.J. Roberts (2013).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/shah_roberts_ISRN_2013.pdf">
Dynamically Measuring Statistical Dependencies in Multivariate Financial Time Series Using Independent Component Analysis.</a><BR>
ISRN Signal Processing. Article 434832. May 2013.

<LI>
J. Calliess, M. Osborne and S.J. Roberts (2013).<BR>
Multi-agent planning with mixed-integer programming and adaptive interaction constraint generation.<BR>
Sixth Annual Symposium on Combinatorial Search, SoCS 2013.

<LI>
I. Psorakis, I. Rezek, Z. Frankel and S.J. Roberts (2013).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/MLDM_BOMP.pdf">
Discovering latent association structure via Bayesian one-mode projection of temporal bipartite graphs.</a><BR>
International Conference on Machine Learning and Data Mining, MLDM-13 (to appear).

<LI>
S. D. Ramchurn, M. A. Osborne, O. Parson, T. Rahwan, S. Maleki, S. Reece, T. D. Huynh, M. Alam, J. Fischer, T. Rodden, L. Moreau, S. J. Roberts (2013) <br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/AAMAS2013-agentswitch.pdf">AgentSwitch: towards smart electricity tariff selection</a>,<BR>
<I>12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)</I>, Saint Paul, USA, 6 - 10 May 2013.


<LI>
E. Simpson, S. Roberts, I. Psorakis and A. Smith (2013).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/galaxyZooSN_simpson_etal.pdf">
Dynamic Bayesian Combination of Multiple Imperfect Classifiers</a><BR>
<I>Decision Making and Imperfection. Intelligent Systems Reference Library series Vol 474</I>. Springer.

<LI>
J. Fischer, S. D. Ramchurn, M. A. Osborne, O. Parson, T. D. Huynh, M. Alam, N. Pantidi, S. Moran, K. Bachour, S. Reece, E. Costanza, T. Rodden and N. R. Jennings (2013). <BR>
<a href="http://www.robots.ox.ac.uk/~mosb/papers/IUI2013-agentswitch-camera-ready.pdf">Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling</a> <BR>
<em>International Conference on Intelligent User Interfaces</em>, Santa Monica, CA, USA, 19 - 22 Mar 2013.


<LI>
M. Dawkins, R. Cain, K. Merelie, S. Roberts (2013). <BR>
In search of Behavioural Correlates of Optical Flow Patterns in the Automated Assessment of Broiler Chicken Welfare.
<I>Applied Animal Behaviour Science</I>.

<LI>
M. Smith, S. Reece, I. Psorakis, I. Rezek, S. Roberts (2013). <BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/KAIS_Marine-Abnormality.pdf">
Maritime Abnormality Detection using Gaussian Processes.</a> <BR>
<I>Knowledge and Information Systems, July 2013.</I>

</UL>

<H3><a NAME="tag2012">2012</a></H3>
<ul>

<LI>
M. A. Osborne, D. Duvenaud, R. Garnett, Carl E. Rasmussen, S. J. Roberts, Z. Ghahramani (2012).<BR>
<a href="http://www.robots.ox.ac.uk/~mosb/papers/bbq_nips_final.pdf">
Active learning of model evidence using Bayesian quadrature</a> <BR>
<em>Advances in Neural Information Processing Systems 26 (NIPS 2012)</em>, December 2012, Lake Tahoe, USA.

<LI>
I. Psorakis, I. Rezek, Z. Frankel and S. J. Roberts (2012). <BR>
<a href="http://arxiv.org/abs/1212.2767">
Bayesian one-mode projection for dynamic bipartite graphs</a><BR>
<I>arXiv:1212.2767, 2012.</I>

<LI>
M. Smith, S. Reece, S. Roberts and I. Rezek (2012).<BR>
<a href="pubs/OnlineGaussianProcessEVT.pdf">
Online Maritime Abnormality Detection using Gaussian Processes and Extreme Value Theory</a><BR>
<I>IEEE International Conference on Data Mining, 2012.</I> <B>Best paper award</B>

<LI>

S. Roberts, M. Osborne, M. Ebden, S. Reece, N. Gibson and S. Aigrain (2012).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/philTransA_2012.pdf">
Gaussian Processes for Timeseries Modelling</a><BR>
<I>Philosophical Transactions of the Royal Society (Part A)</I>. Published online December 31, 2012 doi: 10.1098/rsta.2011.0550,
Phil. Trans. R. Soc. A 13 February 2013 vol. 371 no. 1984 20110550

<LI>
J. Calliess, M. Osborne and S. Roberts (2012).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/calliess_ICML_workshop_2012.pdf">
Towards auction-based multi-agent collision-avoidance under continuous stochastic dynamics</a><BR>
<I>ICML-12 Workshop on Markets, Mechanisms, and Multi-Agent Models, July 2012.</I>

<LI>
S. Roberts, R. Cain and M. Dawkins (2012).<BR>
<a href="http://rsif.royalsocietypublishing.org/content/early/2012/08/31/rsif.2012.0594.short?rss=1">
Prediction of welfare outcomes for broiler chickens using
Bayesian regression on continuous optical flow data
</a><BR>
<I>Journal of the Royal Society, Interface</I>.

<LI>
Nicholson, D., Reece, S., Rogers, A., Roberts, S. J., & Jennings, N. R. (2012).<BR>
Distributed Data Fusion Design: Overarching Design Concerns and Some New Approaches.<BR>
In D. Hall, C-Y. Chong & J. Llinas & M. Liggins (Eds.), Distributed Data Fusion for Network-Centric Operations (pp. 17-46). CRC Press.<BR>

<LI>
M. Ebden, T.D. Huynh, L. Moreau, S. Ramchurn and S. Roberts (2012).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/ipaw2012EbdenHuynh.pdf">
Network Analysis on Provenance Graphs from a Crowdsourcing Application</a><BR>
<I>Proceedings of IPAW 2012</I>, Springer LNCS.

<LI>
I. Psorakis, S. Roberts, I. Rezek and B. Sheldon (2012).<BR>
<a href="http://rsif.royalsocietypublishing.org/content/early/2012/06/07/rsif.2012.0223">Inferring social network structure in ecological systems from
spatio-temporal data streams</a>
<BR>
<I>Journal of the Royal Society, Interface.</I>

<LI>
J. Calliess, M. Osborne and S. Roberts (2012).<BR>
<a href="pubs/TowardsOptimization.pdf">
Towards optimization-based multi-agent collision-avoidance under continuous
stochastic dynamics.</a><BR>
<I>Proceedings of the AAAI-12 Workshop on Multiagent Pathfinding.</I> Toronto, July 2012.

<LI>
M. Osborne, R. Garnett, K. Swersky, and N. de Freitas (2012).<BR>
<a href="pubs/Osborne_Garnett_Swersky_de_Freitas_fault_bucket_aaai_2012.pdf">
Prediction and fault detection of environmental signals with uncharacterised faults.</a><BR>
<I>Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)</I>

<LI>
D. Lyons, J. Calliess and U. Hanebeck (2012).<br>
<a href="https://css.paperplaza.net/conferences/scripts/abstract.pl?ConfID=44&Number=1107">
Chance Constrained Model Predictive Control for Multi-Agent Systems with Coupling Constraints.</a>
<BR>
<I>Proc. of American Control Conference (ACC' 2012)</I> (to appear).

<LI>
M. Osborne, R. Garnett, S. Roberts, C. Hart, S. Aigrain, and N. Gibson (2012).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/bqr_aistats_2012.pdf">
Bayesian Quadrature for Ratios.</a>
<BR>
<I>Proceedings of AISTATS 2012</I>. An extended version is also available: <a href="http://www.robots.ox.ac.uk/~mosb/papers/BQ_aistats_appendix.pdf">
Bayesian quadrature for ratios: now with even more Bayesian quadrature</a><BR>

<LI>
A. McQuillan, S. Aigrain, and S. Roberts (2012).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/kepler_q1_arc_a_and_a.pdf">
Statistics of Stellar Variability from Kepler - I: Revisiting Quarter 1 with an Astrophysically Robust Systematics Correction. http://dx.doi.org/10.1051/0004-6361/201016148
</a><BR>
<I>Astronomy and Astrophysics.</I> 539, A137<BR>

<LI>
M. Dawkins, R. Cain and S. Roberts (2012).<BR>
<a href="http://dx.doi.org/10.1016/j.anbehav.2012.04.036">Optical flow, flock behaviour and chicken welfare.</a><BR>
<I>Animal Behaviour</I>. 84(1), 219-223.

</UL>

<H3><a NAME="tag2011">2011</a></H3>
<ul>

<LI>
E. Simpson, S. Roberts, I. Psorakis, A. Smith and C. Lintott (2011).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/vbibcc_workshop.pdf">
Bayesian Combination of Multiple, Imperfect Classifiers.</a>
<BR>
<I>Proceedings of NIPS 2011 workshop.</I>

<LI>
E. Roussos, S. Roberts and I. Daubechies (2011).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/MLINI-2011_text_final.pdf">
Variational Bayesian Learning of Sparse Representations and its Application in Functional Neuroimaging.</a>
<BR>
<I>Proceedings of NIPS 2011 workshop.</I>

<LI>
W. Armour, A. Karastergiou, M. Giles, C. Williams, A. Magro, K.
Zagkouris, S. Roberts, S. Salvini, F. Dulwich and B. Mort (2011).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/gpu_radio_transients_2011.pdf">
A GPU-based survey for millisecond radio transients using ARTEMIS.
</a><BR>
<I>Proceedings of ADASS XXI.</I><BR>

<LI>
N. P. Gibson, S. Aigrain, S. Roberts, T. M. Evans, M. Osborne and
F. Pont (2011).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/Gibson_NICMOS_GP.pdf">
A Gaussian process framework for modelling instrumental systematics: application to transmission spectroscopy.
</a><BR>
<I>Monthly Notices of the Royal Astronomical Society.</I><BR>

<LI>
M.A. Osborne, S.J. Roberts, A. Rogers, and N.R. Jennings (2011).<BR>
<a href="pubs/tosn_gp.pdf">
Real-Time Information Processing of Environmental Sensor Network Data
using Bayesian Gaussian Processes
</a><BR>
<i>Transactions on Sensor Networks</i><BR>

<LI>
J-P. Calliess, D. Lyons and U. Hanebeck (2011).<BR>
<a href="http://www.robots.ox.ac.uk/~jan/papers/PARG_01_11.pdf">
Lazy auctions for multi-robot collision avoidance and motion control under
uncertainty.</a><BR>
Technical report PARG-11-01<BR>

<LI>
D. Lyons, J-P. Calliess and U.D. Hanebeck (2011).<BR>
<a href="http://arxiv.org/abs/1104.5384">
Chance-constrained Model Predictive Control for Multi-Agent Systems.</a><BR>
<i>preprint</i> arXiv:1104.5384 <BR>

<LI>
I. Psorakis, S. Roberts, M. Ebden and B. Sheldon (2011).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/PRE_NMF.pdf">
Overlapping Community Detection using Bayesian Nonnegative Matrix Factorization.</a><BR>
<i>Physical Review E.</i> Online version available via http://pre.aps.org/abstract/PRE/v83/i6/e066114<BR>

<LI>
S. Reece, S. Roberts, D. Nicholson and C. Lloyd (2011).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/gp_rvm_fusion_2011.pdf">
Determining intent using hard/soft data and Gaussian process classifiers.</a><BR>
<i>Proceedings of Fusion 2011.</i>. <BR>

<LI>
C. Fox and S. Roberts (2011).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/fox_vbtut.pdf">
A Tutorial on Variational Bayesian Inference.</a><BR>
<i>Artificial Intelligence Review. Spinger.</i> 38(2), 85-95. (DOI) 10.1007/s10462-011-9236-8<BR>

<LI>
A. Karastergiou, S. J. Roberts, S. Johnston, H. Lee, P. Weltevrede and M. Kramer (2011). <BR>
<a href="pubs/Atransient.pdf">
A transient component in the pulse profile of PSR J0738-4042.</a><BR>
<i>Monthly Notices of the Royal Astronomical Society.</i><BR>
doi:10.1111/j.1365-2966.2011.18697.x<BR>

<LI>
J.W. Yoon, S. J. Roberts, M. Dyson and J. Gan (2011).<BR>
<a href="http://www.ncbi.nlm.nih.gov/pubmed/21493037">
Bayesian Inference for an adaptive Ordered Probit model: an application to Brain Computer
Interfacing.</a><BR>
<I>Neural Networks</I>, 24, 726-734.<BR>

<LI>
M. Ebden and S. Roberts (2011).<BR>
<a href="http://dx.doi.org/10.1016/j.adhoc.2010.06.002">
Graph Marginalization for Rapid Assignment in Wide-area Surveillance.</a><BR>
Adhoc Networks Journal 9(2): 180-8.<BR>

<LI>
H-J. Lee, S.J. Roberts, K.A. Drake, and M. Stamp Dawkins (2011).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/hens_feather_peck_optical_flow_rsi_2010.pdf">
Prediction of feather damage in laying hens using optical flows and Markov
models.</a><BR>
Journal of the Royal Society Interface 8(57): 489-499.<BR>

</ul>
<H3><a NAME="tag2010">2010</a></H3>
<ul>

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<LI>I. Psorakis, S. Roberts and B. Sheldon (2010).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/commDetNips2010.pdf">
Soft Partitioning in Networks via Bayesian Non-negative Matrix Factorization</a><BR>
NIPS 2010 Workshop "Networks Across Disciplines in Theory and Applications".
<BR>

<LI>S. Reece, R. Mann, I. Rezek and S. Roberts (2010).<BR>
<a href="pubs/MaxEnt10_Reece.pdf">Gaussian Process Segmentation of
Co-Moving Animals.</a><BR>
30th Intenational Workshop on Bayesian Inference and Maximum Entropy
Methods in Science and Engineering (MaxEnt 10), Chamonix, France, July
4-9, 2010.
<BR>

<LI>I. Psorakis, S. Roberts, B. Sheldon (2010).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/BayesianCommunityDetectionusingNMF.pdf">
Efficient Bayesian Community Detection using Non-negative Matrix Factorisation.</a><BR>
arXiv:1009.2646 PARG-10-02.
<BR>

<LI>I. Psorakis, T. Damoulas, M. A. Girolami (2010).<BR>
<a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5559460">
Multiclass Relevance Vector Machines: Sparsity and Accuracy.</a><BR>
IEEE Transactions on Neural Networks, Vol. 21, No 10, pp. 1588-1598.
<BR>

<LI>L. Pickup, D. Capel, S. Roberts, A. Zisserman (2010).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/super_res_chapter_2010.pdf">
Multiframe Super-Resolution from a Bayesian Perspective.</a><BR>
In <i>Super-Resolution Imaging</i>, Chapter 9, pages 247-284, CRC Press.
<BR>

<LI>R. Mann, R. Freeman, M. Osborne, R. Garnett, C. Armstrong,
J. Meade, D. Biro, T. Guilford and S. Roberts (2010).<BR>
<a href="http://rsif.royalsocietypublishing.org/content/early/2010/07/22/rsif.2010.0301.abstract">Objectively
identifying landmark use and predicting flight trajectories of the
homing pigeon using Gaussian processes</a><BR>
Journal of the Royal Society, Interface. 8(55), 210-219.
<BR>

<LI>M. Kaufman and S. Roberts (2010).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/aamas2010_coord_vs_info.pdf">
Coordination vs. Information in Multi-agent Decision Processes.</a><BR>
AAMAS 2010.
<BR>

<LI>R. McInerney, S. Roberts and I. Rezek (2010).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/aamas2010-rmcinerney.pdf">
Sequential Bayesian Decision Making for Multi-armed Bandit.</a><BR>
AAMAS 2010.
<BR>

<LI>S. Reece and S. Roberts (2010).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/cam_gp.pdf">
The Near Constant Acceleration Gaussian Process Kernel for Tracking.</a><BR>
<I>IEEE Signal Processing Letters</I>.
<BR>

<LI>M. Ebden, A. Stranjak and S. Roberts (2010).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/wmm.pdf">
Visualizing Uncertainty in Reliability Functions with Application to Aero Engine Overhaul.</a><BR>
<I>Journal of the Royal Statistical Society C. Volume 59, part 1 (2010), pages 163-173.</I>
<BR>

<LI>
S. Reece and S. J. Roberts (2010). <BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/Fusion2010_0132.pdf">
An Introduction to Gaussian Processes for the Kalman Filter Expert.</a><BR>
Proceedings of Fusion 2010..
<BR>

<LI>
R. Garnett, M. A. Osborne, S. J. Roberts (2010). <BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/ipsn673-garnett.pdf">
Bayesian Optimization for Sensor Set Selection</a>,<BR>
IPSN 2010, Stockholm.
<BR>

<LI>
R. Garnett, M. A. Osborne, S. Reece, A. Rogers and S. J. Roberts (2010). <BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/journal_changept.pdf">
Sequential Bayesian Prediction in the Presence of Changepoints and Faults</a>,<BR>
The Computer Journal.
<BR>

<LI>
M. A. Osborne, R. Garnett, and S. J. Roberts (2010) <BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/FaultySelection.pdf">
Active data selection for sensor networks with faults and changepoints</a>. <BR>
IEEE 24th International Conference on Advanced Information Networking and Applications (AINA 2010), April 2010, Perth, Australia.
<BR>

<LI>M. A. Osborne and R. Garnett and S. J. Roberts (2010). <BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/SBQ_techreport.pdf">Sampling for Bayesian Quadrature</a>
, Technical Report PARG-10-01.
<BR>

<LI>J. W. Yoon and S. Roberts (2010).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/voronoi_tracking.pdf">
Robust Measurement Validation in Target Tracking using Geometric Structure.</a><BR>
<I>IEEE Signal Processing Letters</I>.<br>
<a href="http://dx.doi.org/10.1109/LSP.2010.2041695">doi link</a>
<BR>

<li>M. Ebden and S. Roberts (2010).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/AdHocNetworks_2010.pdf">
Graph Marginalization for Rapid Assignment in Wide-area Surveillance.</a><BR>
In: International Conference on Ad Hoc Networks, Niagara Falls, Canada, 23-25 September 2009.<BR>
LNICST (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) 28: 691-703, 2010.
<BR>

<li>S. M. Lee and S. Roberts (2010).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/dynClassLee2010.pdf">
Sequential Dynamic Classification Using Latent Variable Models.</a><br>
The Computer Journal 2010; doi: 10.1093/comjnl/bxp127
<BR>

<li>D. Lowne, S. Roberts and R. Garnett (2010).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/pr_dlr.pdf">
Sequential Non-stationary Dynamic Classification with Sparse
Feedback.</a><br>
<i>Pattern Recognition. Volume 43, Issue 3, March 2010, Pages 897-905.</i>
<BR>

</ul>

<H3><a NAME="tag2009">2009</a></H3>
<ul>

<LI>R. Mann, R. Freeman, M. A. Osborne, R. Garnett, J. Meade, C. Armstrong, D. Biro, T. Guilford and S. J. Roberts (2009)<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/mann_maxent_final.pdf">
Gaussian Processes for Prediction of Homing Pigeon Flight Trajectories</a>,<BR>
Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP Conf. Proc., Ann Arbor. July, 2009.
<BR>

<LI>R. Garnett, M. A. Osborne and S. J. Roberts (2009)<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/changepoint.pdf">
Sequential Bayesian prediction in the presence of changepoints</a><BR>
In: 26th International Conference on Machine Learning (ICML 2009), June 2009, Montreal, Canada.
<BR>

<LI>M. A. Osborne, R. Garnett and S. J. Roberts (2009)<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/OsborneGarnettRobertsGPGO.pdf">
Gaussian processes for Global Optimization</a>.<BR>
In: 3rd International Conference on Learning and Intelligent Optimization (LION3), January 2009, Trento, Italy.
<BR>

<LI>J.W. Yoon, S. Roberts, M. Dyson and J. Gan (2009).<BR>
<a href="http://dx.doi.org/10.1016/j.neunet.2009.06.005">
Adaptive Classification for Brain Computer Interface systems using Sequential
Monte Carlo Sampling.</a><BR>
<I>Neural Networks: Special Issue on Brain Machine Interfaces. Vol 22. p1286-1294.</I>
<BR>

<li>M. Dawkins, H-J Lee, C. Waitt and S. Roberts (2009).<BR>
Optical flow patterns in broiler chicken flocks as automated measures of behaviour and gait.<BR>
<i>Applied Animal Behaviour Science</I> 119 p203-209.
<BR>

<li>S. Reece, S. Roberts, C. Claxton & D. Nicholson. (2009).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/fusion09.pdf">
Multi-sensor fault recovery in the presence of known and unknown fault types.</a><BR>
Proceedings of Fusion 2009.
<BR>
</LI>

<li>T. Guilford, J. Meade, J. Willis, R.A. Phillips, D. Boyle, S. Roberts, M.
Collett, R. Freeman & C.M. Perrins. (2009).<BR>
Analysing the migratory movements of a small pelagic seabird, the Manx
Shearwater Puffinus puffinus.<BR>
Proceedings of the Royal Society B.
<BR>
</LI>

<LI>C.S.L. Tsui, J.Q. Gan and S.J. Roberts (2009).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/tsui_etal_BCI_2009.pdf">
A Self-paced Brain-Computer Interface for Controlling a Robot Simulator:
An Online Event Labelling Paradigm and an Extended Kalman Filter Based
Algorithm for Online Training.</a><BR>
<I>Medical and Biological Engineering and Computing.</I> vol 47, num 3. p257-265.
<BR>
</LI>

<LI>S. Reece, R. Garnett, M. Osborne and S. Roberts (2009).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/anomalyDetection_nonStat_GP_reece_etal.pdf">
Anomaly Detection and Removal using Non-Stationary Gaussian Processes.</a><BR>
Technical Report PARG-2009-1.
<BR>
</ul>

<H3><a NAME="tag2008">2008</a></H3>
<ul>

<li>J.W. Yoon, S.J. Roberts, M. Dyson and J. Gan (2008).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/ideal.pdf">
Adaptive Classification by Hybrid EKF with Truncated Filtering: Brain
Computer Interfacing.</a><BR>
Intelligent Data Engineering and Automated Learning - IDEAL
2008. Lecture Notes in Computer Science, Vol 5326/2008,
370-377. Springer.
<BR>
</LI>

<li>I. Rezek, D. Leslie, S. Reece, S. Roberts, A. Rogers, R. Dash and N. Jennings (2008).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/jair08.pdf">
On Similarities between Inference in Game Theory and Machine Learning.</a><br>
Journal of Artificial Intelligence Research, Vol 33, 259-283.
<br>
</li>

<li>S.M. Lee and S.J. Roberts (2008).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/TechReport_PARG0803.pdf">
Multivariate Time Series Forecasting in Incomplete Environments.</a><br>
Technical Report PARG-08-03.
<br>
</li>

<li>S.M. Lee and S.J. Roberts (2008).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/TechReport_PARG0802.pdf">
Sequential Dynamic Classification using Latent Variable Models.</a><br>
Technical Report PARG-08-02.
<br>
</li>

<li>H.J. Lee and S.J. Roberts (2008).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/LeeRoberts_EVT.pdf">
On-line Novelty Detection Using the Kalman Filter and Extreme Value Theory.</a><BR>
Proceedings of International Conference on Pattern Recognition, ICPR 2008.
<BR>
</LI>

<li>N. Shah and S.J. Roberts (2008).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/ICArn2008_Shah_Roberts_Camera_Ready_Version.pdf">
Hidden Markov Independent Component Analysis as a Measure of Coupling in Multivariate Financial Time Series.</A><BR>
Proceedings of ICA Research Network International Workshop 2008.
<BR>
</LI>

<li>M. Ebden, M. Briers and S. Roberts (2008).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/dcf.pdf">
Decentralized Predictive Sensor Allocation.</A><BR>
Proceedings of IEEE Conference on Decision and Control, Dec. 2008, 1702-1707.
<BR>
</LI>

<li>J.W. Yoon, S. Roberts, M.Dyson, J. Gan (2008).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/Yoon_MFI2008.pdf">
Sequential Bayesian Estimation for Adaptive Classification</A><BR>
Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)
<BR>
</LI>

<li>R. Garnett and S.J. Roberts (2008).<br>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/TR-PARG-08-01.pdf">
Learning from Data Streams with Concept Drift.</a><br>
Technical Report PARG-08-01.
<br>
</li>

<li>N.M. Adams M. Field, E. Gelenbe, D.J. Hand, N.R. Jennings, D.S. Leslie, D. Nicholson, S.D. Ramchurn, S.J. Roberts, A. Rogers. (2008). <BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/RISE08.pdf">
The ALADDIN Project: Intelligent Agents for Disaster Management.</a><BR>
Proceedings of RISE-08.
<br>
</li>

<li>A. Stranjak, P. Dutta, M. Ebden, A. Rogers, and P. Vytelingum (2008).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/AAMAS_2008.pdf">A Multi-agent Simulation System for Prediction and Scheduling of
Aero Engine Overhaul.</a><BR>
In: the Seventh International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS '08), Estoril, Portugal, May 2008. 81-88.
<BR>

<li>S. Reece and S. Roberts (2008).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/gcu_final_2008.pdf">
Generalised Covariance Union: A Unified Approach to Hypothesis Merging
in Tracking.</a><BR>
<I>IEEE Transactions on Aerospace and Electronic Systems.</I> 46(1), 207-221.
<BR>
</LI>

<li>T. Guilford, J. Meade, R. Freeman, D. Biro, T. Evans, F. Bonadonna, D. Boyle, S. Roberts, C Perrins (2008).<br>
GPS tracking of the foraging movements of Manx Shearwaters Puffinus puffinus
breeding on Skomer Island, Wales.<br>
<i>Ibis</i>, 150(3), 462-473.
<BR>
</LI>

<li>C. Fox and S. Roberts (2008).<BR>
ThomCat: A Bayesian Blackboard model of Hierarchical Temporal
Perception.<BR>
Proceedings of AAAI 2008.
<BR>
</LI>

<LI> M. Osborne, S. Roberts, A. Rogers, S. Ramchurn and N. Jennings
(2008).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/osborne-GaussianProcesses.pdf">
Towards Real-Time Information Processing of Sensor Network Data
using Computationally Efficient Multi-output Gaussian Processes.</a><BR>
Proceedings of IPSN'08, 109-120.
<BR>
</LI>

<li>A. Rogers, M. A. Osborne, S. D. Ramchurn, S. J. Roberts and N. R. Jennings (2008).<br>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/rogers-InformationAgents.pdf">
Information Agents for Pervasive Sensor Networks.</A><br>
Proceedings of PerSens 2008.
<BR>
</LI>

<li>C. Fox, I. Rezek and S.J. Roberts (2008).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/qbayes2.pdf">
Local Quantum Computing for Fast Probably MAP Inference in Graphical
Models.</a><br>
Proceedings of Second Quantum Interaction Symposium. Oxford, UK, April 2008.
<br>
</li>

</ul>

<H3><a NAME="tag2007">2007</a></H3>

<ul>

<li>M. Osborne and S.J. Roberts (2007).<br>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/PARG-07-01.pdf">
Gaussian Processes for Prediction.</a><br>
Technical Report PARG-07-01.
<br>
</li>

<li>L. Pickup, D. Capel, S. Roberts and A. Zisserman (2007).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/pickup07a.pdf">Bayesian Methods for Image Super-Resolution.</a><br>
The Computer Journal.
<br>
</li>

<li>M.A. Little, P.E. McSharry, S.J Roberts, D.A.E. Costello, I.M. Moroz (2007).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/nonLinRecSpeech.pdf">
Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection</a></br>
BioMedical Engineering OnLine 2007, 6:23 (26 June 2007).
<br>
</li>

<li>L. Pickup, D. Capel, S. Roberts and A. Zisserman (2007).<br>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/eurasipSuperRes2007.pdf">
Overcoming Registration Uncertainty in Image Super-Resolution: Maximize or Marginalize?</a><br>
EURASIP Journal on Advances in Signal Processing.
<br>
</li>

<li>C. Fox, I. Rezek and S. Roberts (2007).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/DrumNBayes.pdf">
Drum 'n' Bayes: On-Line Variational Inference for Beat Tracking and Rhythm Recognition.</a><br>
Proceedings of the 2007 International Computer Music Conference.
<br>
</li>

<a href="http://www.robots.ox.ac.uk/~parg/pubs/chmm_ieeproc00.pdf">


<li>I. Rezek, S.J. Roberts and R. Conradt (2007).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/vbarmog_emb05.pdf">
Generative Polyspectral Models for Depth of Anaesthesia Assessment.</a><BR>
Engineering in Medicine and Biology Magazine 26(2):64-73, 2007.
<br>
</li>

<li>D. Biro, R. Freeman, J. Meade, S. Roberts and T. Guilford (2007).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/pnasPaper.pdf">
Pigeons combine compass and landmark guidance in familiar route navigation.</a><br>
Proceedings of the National Academy of Sciences (PNAS), 104(18), 7471-7476.
<br>
</li>

<li>I. Proudler, S. Roberts, S. Reece and I. Rezek (2007).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/dsp07.pdf">
An Iterative Signal Detection Algorithm based on Bayesian Belief Propagation Ideas.</a><br>
Proceedings of DSP2007, July 2007, 355-358.
<br>
</li>

<li>S. Reece, A. Rogers, S. Roberts and N. R. Jennings (2007).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/aaai2007.pdf">
A Multi-Dimensional Trust Model for Heterogeneous Contract Observations.</a><br>
Proceedings of Twenty-Second AAAI Conference on Artificial Intelligence (AAAI-07), vol 1, 128-135.
<br>
</li>

<li>S. Reece, A. Rogers, S. Roberts and N. R. Jennings (2007).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/trustPaper.pdf">
Rumours and Reputation: Evaluating Multi-Dimensional Trust within a Decentralised Reputation System.</a><br>
Proceedings of AAMAS 07, num 165.
<br>
</li>

</ul>

<H3><a NAME="tag2006">2006</a></H3>
<ul>

<li>L.C. Pickup, S.J. Roberts, and A. Zisserman (2006).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/pickup06BMVC.pdf">
Optimizing and Learning for Super-resolution</a><br>
<i>Proceedings of the British Machine Vision Conference.</i>
<br>
</li>

<li>D.R. Lowne, I. Rezek and S.J. Roberts (2006).<br>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/maia06.pdf">
Adaptive Classification of EEG Features with Sparse Feedback.</a><br>
Proc. MAIA Brain Computer Interfaces Workshop 2006, Rome, Italy.
<br>
</li>

<li>L.C. Pickup, D.P. Capel, S.J. Roberts, and A. Zisserman (2006).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/pickup06aNIPS.pdf">
Bayesian Image Super-Resolution, Continued</a><br>
<i>Advances in Neural Information Processing Systems, NIPS-06.</i>
<br>
</li>

<li>K. Lees,S. Roberts, P. Skamnioti and S. Gurr (2006).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/angDist.pdf">
Gene Microarray Analysis using Angular Distribution Decomposition.</a><br>
<i>Journal of Computational Biology.</i> Vol 14(1), p68-83.
<br>
</li>

<li>W. Addison and S. Roberts (2006).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/addison_roberts.pdf">
Blind Source Separation with Non-stationary mixing using Wavelets.</a><br>
<i>6th International Conference on Independent Component Analysis and
Blind Source Separation.</i>
<br>
</li>

<li>D. Lowne, C. Haw and S. Roberts (2006).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/GrazDynClassFinal.pdf">
An adaptive, sparse-feedback EEG classifier for self-paced BCI.</a><br>
<i>Graz 2006 conference on Brain Computer Interfaces</i>
<br>
</li>

<li>C. Haw, D. Lowne and S. Roberts (2006).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/grazBP2006.pdf">
User specific template matching for event detection using single channel EEG.</a><br>
<i>Graz 2006 conference on Brain Computer Interfaces</i>
<br>
</li>

<li>C. Orphanidou, I. Moroz and S. Roberts (2006).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/orphanidouAlgApprox.pdf">
Multiscale Voice Morphing using Radial Basis Function Analysis.</a> <br>
<i>Algorithms for Approximation. Iske and Levesley (Eds.). Springer.</i> pp 61-69.
<br>
</li>

<li>S. Mukherjee and S. Roberts (2006).<br>
A decision-theoretic analysis of differential gene expression.<br>
Technical Report TR-PARG-06-01.<br>
</li>

<li>K.K. Lau, S. Roberts, D. Biro, R. Freeman, J. Meade and T. Guilford (2006).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/edgeJTB.pdf">
An edge detection approach to investigating pigeon navigation.</a><br>
<i>Journal of Theoretical Biology.</i>. Volume 239, Issue 1, 7 March 2006, Pages 71-78.
<br>
</li>

<li>M. Little, P. McSharry, M. Moroz, and S. Roberts (2006).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/NonlinearBiophysicalVoiceDisorderDetection.pdf">
Nonlinear, Biophysically-Informed Speech Pathology Detection.</a><br>
Proceedings of IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP) 2006. Vol 2, 1080-1083.
<br>
</li>

<li>M. Little, P. McSharry, M. Moroz, and S. Roberts (2006).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/spsurr_distrib.pdf">
Testing the assumptions of linear prediction analysis in normal vowels</a><br>
<i>J. Acoust. Soc. Am. 119(1), 549-558.</i>.
<br>
</li>

<LI>A. Rogers, R.K. Dash, N.R. Jennings, S. Reece, and S. Roberts (2006).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/sensors.pdf">
Computational Mechanism Design for Information Fusion within Sensor Networks.
</a>
<BR>
Ninth International Conference of Information Fusion, Florence, Italy, 2006.
<BR></LI>

<LI>A. Rogers, R.K. Dash, S. Reece, S. Roberts, and N.R. Jennings (2005).<BR>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/AAMAS_Demonstration.pdf">
Computational Mechanism Design for Multi-Sensor Information Fusion.
</a>
<BR>
In Proceedings of the Fifth Joint International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), pp 1463-4, Hakodate, Japan, 2006
<BR></LI>
</ul>

<H3><a NAME="tag2005">2005</a></H3>
<ul>

<li>M. Briers, S. R Maskell, S. Reece, S. Roberts, I. Rezek, V.D. Dang, A. Rogers, and N.R. Jennings (2005).<br>
<a href="http://www.robots.ox.ac.uk/~argus/papers/Dynamic%20sensor%20coalition%20formation%20to%20assist%20the%20distributed%20tracking%20of%20targets:%20Application%20to%20wide-area%20surveillance.pdf">Dynamic sensor coalition formation to assist the distributed tracking of targets: Application to wide-area surveillance</a>.<br>
IEE Conference on Signal Processing Solutions for Homeland Security, 11 October 2005.
<br>
</li>

<li>E. Roussos, S.J. Roberts, and I. Daubechies (2005).<br>
<A HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/vbwtica.pdf">Variational
Bayesian Learning for Wavelet Independent Component Analysis.</A><br>
AIP Conference Proceedings, Bayesian Inference and Maximum Entropy Methods in
Science and Engineering, Vol. 803, Nov. 23, 2005, pp. 274-281.
<br>
</li>

<li>I. Rezek, S.J. Roberts, E. Siva and R. Conradt (2005).<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/vbarmog_icmla05.pdf">
Depth of Anaesthesia Assessment with Generative Polyspectral Models .</A><br>
Proc. 4th International Conference on Machine Learning and Applications,
Los Angeles, USA.
<br>
</li>

<li>I. Rezek, S.J. Roberts, A. Rogers, R.K. Dash and N.R. Jennings (2005).<br>
<A HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/fusion05.pdf"> Unifying Learning in Games and Graphical
Models.</A><br>
Proc. 8th International Conference on Information Fusion, Philadelphia, USA, July 2005.
<br>
</li>

<li>I. Rezek and S. J. Roberts (2005).<br>
<A HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/varhmm.ps.gz">
Ensemble Hidden Markov Models with Extended Observation Densities for Biosignal Analysis.</A><br>
in <I>Probabilistic Modeling in Biomedicine and Medical Bioinformatics</I>, Eds Dirk Husmeier, Richard Dybowski, and Stephen Roberts, Springer Verlag, 2005
<br>
</li>

<li>P. Sykacek, I. Rezek and S. J. Roberts (2005).<br>
<A HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/sensfbook.pdf">
Bayes Consistent Classification of EEG Data by Approximate Marginalization.</A><br>
in <I>Probabilistic Modeling in Biomedicine and Medical Bioinformatics</I>, Eds Dirk Husmeier, Richard Dybowski, and Stephen Roberts, Springer Verlag, 2005
<br>
</li>

<li>M. Little, P. McSharry, M. Moroz, and S. Roberts (2005).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/lnai_nolisp05.pdf">
A simple quasi-linear discrete model of vocal fold dynamics.</a><br>
<i>Lecture Notes in Computer Science (Springer-Verlag), NOLISP 2005, pp. 348-356.</i>.
<br>
</li>

<li>R. K. Dash, A. Rogers, S. Reece, S. J. Roberts and N. R. Jennings (2005).<br>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/constBandwidthAlloc.pdf">
Constrained Bandwidth Allocation in Multisensor Information Fusion: A Mechanism Design Approach.</a><br>
<i>Proceedings of The Eighth International Conference on Information Fusion, Philadelphia, USA.</i> July 2005.
<br>
</li>

<li>M. Little, P. McSharry, M. Moroz, and S. Roberts (2005).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/nolisp05.pdf">
A simple nonlinear model of vocal fold dynamics.</a><br>
<i>Proceedings of 3rd International Conference on Non-Linear Speech
Processing, Barcelona, Spain, April, pp. 188-203.</i>
<br>
</li>

<li>I. Rezek, S. Roberts, A. Rogers, R. Dash, N. Jennings (2005).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/mlandgames_fusion05.pdf">
Unifying Learning in Games and Graphical Models.</a><br>
<i>Proceedings of Fusion-05.</i>
<br>
</li>

<li>S. Reece, S. Roberts (2005).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/fusion05a.pdf">
Robust, Low-bandwidth, Multi-vehicle Mapping.</a><br>
<i>Proceedings of Fusion-05.</i>
<br>
</li>

<li>S. Reece, D. Nicholson (2005).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/fusion05b.pdf">
Tighter Alternatives to the Cramer-Rao Lower Bound for Discrete-time Filtering.</a><br>
<i>Proceedings of Fusion-05.</i>
<br>
</li>

<li>S. Mukherjee, S.J. Roberts and M.J. van der Laan (2005).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/bioinf05.pdf">Data adaptive test statistics for microarray data.</a><br>
<i>Bioinformatics, 21(suppl 2) ii108-ii114.</i>
<br>
</li>

<li>C. Orphanidou, I. Moroz and S. Roberts (2005).<br>
Multiscale Voice Morphing using Radial Basis Function Analysis.<br>
<i>Proceedings of 7th International Symposium on Algorithms for Approximation.</i>
<br>
</li>

<li>S. Roberts &amp R. Choudrey (2005).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/ica_eeg_pos.pdf">
Bayesian Independent Component Analysis with Constraints:
an application in Biosignal Analysis</a> <br>
<i>Lecture Notes in Computer Science, special issue on
Machine Learning.</i>
<br>
</li>

<li>S. Mukherjee, S.J. Roberts and M.J. van der Laan (2005).<br>
Data adaptive test statistics for microarray data.<br>
<i>Proceedings of the 9th Annual Int. Conference in Research in Computational Molecular Biology (RECOMB 2005)</i>
<br>
</li>

<li>S. Mukherjee &amp S. J. Roberts (2005).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/mukherjee_roberts_jbcb_revised.pdf">A Theoretical Analysis of the Selection of
Differentially Expressed Genes.</a> <br>
<i>Journal of Bioinformatics and Computational Biology.</i> Vol. 3, No. 3 (2005) 627-643.
<br>
</li>

<li>J. Ma, C. Orphanidou, I. Moroz, S. Roberts (2005).<br>
Multiscale Voice Morphing using the Complex Wavelet Transform.<br>
<i>Technical report</i>
<br>
</li>

</ul>

<H3><a NAME="tag2004">2004</a></H3>
<ul>

<li>M.A. Little, I.M. Moroz, P.E. McSharry, S.J. Roberts (2004).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/mathsigproc.pdf">Variational Integration for Speech Signal Processing.</a> <br>
<i>Proceedings of IMA Conference on Mathematics in Signal Processing VI.</i>
<br>
</li>

<li>C. Orphanidou, I. Moroz &amp S. Roberts (2004).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/WSEAS_orphanid.pdf">Wavelet-based Voice Morphing.</a> <br>
<i>WSEAS Journal on Systems, 10(3), 3297-3302.</i>
<br>
</li>

<li>S. Mukherjee &amp S. J. Roberts (2004).<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/TR-PARG-04-02.ps.gz">
Unsupervised Learning of Ranking Functions for High-Dimensional Data
</A> <BR>
<i>Technical report # PARG-04-02</i>.
<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/TR-PARG-04-02.pdf">[pdf
version]
</A>
<br></li>


<li>S. Mukherjee &amp S. J. Roberts (2004).<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/CSB_2.ps.gz">Probabilistic
Consistency
Analysis for Gene Selection.</A> <BR>
<i>In Proceedings of the
IEEE Computer Society Bioinformatics Conference 2004 (CSB 2004). IEEE
Press.</i>.
<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/CSB_2.pdf">[pdf
version]
</A>
<br></li>


<li>S. Mukherjee &amp S. J. Roberts (2004).<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/CSB_1.pdf">A Theoretical
Analysis of Gene Selection.</A> (pdf only) <BR>
<i>In Proceedings of the
IEEE Computer Society Bioinformatics Conference 2004 (CSB 2004). IEEE Press.</i>.
<br></li>

<li>S. Mukherjee (2004).<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/TR-PARG-04-01.ps.gz">Sampling distribution of the SAM statistic</A>.<br>
<i>Technical report # PARG-04-01</i>.
<BR>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/TR-PARG-04-01.pdf">[pdf version]</A>
<br></li>

<li>S. Roberts, T. Guilford, I. Rezek & D. Biro (2004).<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/bird_1.pdf">Positional Entropy during Pigeon Homing I: application of Bayesian
Latent State Modelling.</A> (pdf only)<br>
<i>Journal of Theoretical Biology</i>, 227(1), 39-50.
<br></li>

<li>T. Guilford, S. Roberts, D. Biro & I. Rezek (2004).<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/bird_2.pdf">Positional Entropy during Pigeon Homing II: navigational
interpretation of Bayesian Latent State Models.</A> (pdf only)<br>
<i>Journal of Theoretical Biology</i>, 227(1), 25-38.
<br></li>

<li>S. J. Roberts, E. Roussos & R. Choudrey (2004).<br>
<A HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/ica_sp.pdf">Hierarchy, Priors And Wavelets: Structure & Signal Modelling using ICA.</A><br>
<i>Signal Processing</i> 84, 283-297.<br></li>

<li> P. Sykacek, S. Roberts & M. Stokes (2004).<br>
<A HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/vkf_bci_tbme.ps.gz">Adaptive BCI based on variational Bayesian Kalman filtering: an
empirical evaluation.</A> <br>
<i>IEEE Transactions on Biomedical Engineering</i>. 51(5). 719-729.
<br></li>

<li>K. Kar, S. Roberts, R. Stone, M. Oldfield, and B. French (2004).<br>
<A HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/2004-01-1418.pdf">Instantaneous Exhaust Temperature Measurements Using Thermocouple
Compensation Techniques.</A><br>
<i>SAE International Conference Paper</i>, paper 2004-01-1418.<br></li>

<li>
Nicholas P. Hughes, Stephen J. Roberts and Lionel Tarassenko (2004).<br>
<a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/embc04.pdf">Semi-Supervised Learning of Probabilistic Models for ECG Segmentation</a>.<br>
In <i>IEEE Engineering in Medicine and Biology Conference (EMBC) 2004</i>
<br>
</li>

<li>E. Curran, P. Sykacek, S. Roberts, W. Penny, M. Stokes,
I. Jonsrude & A. Owen (2004).<br>
Cognitive tasks for driving a Brain Computer Interfacing System: a
pilot study.<br>
<i>IEEE Transactions on Rehabilitation Engineering</i>, 12 (1): 48-5.<br></li>
</ul>

<H3><a NAME="tag2003">2003</a></H3>
<ul>

<li>S. N. Mukherjee, P. Sykacek, S. J. Roberts &amp S. J. Gurr
(2003).<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/sach03a.pdf">Gene
Ranking Using
Bootstrapped P-values</A>. (pdf only)<br>
<i>ACM SIGKDD Explorations</i>, Volume 5, Issue 2,
Special Issue on Microarray Data Mining.<br></li>

<li>P. Sykacek, S. Roberts, M. Stokes, E. Curran, M. Gibbs and L. C. Pickup. (2003).<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/bci2003.pdf">Probabilistic methods in BCI research.</A> (pdf only)<br>
<i>IEEE Trans. Neural Systems and Rehabilitation Engineering</i>, pp 192-195.<br></li>


<li> N. P. Hughes, S. J. Roberts & L. Tarassenko (2003).<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/nph_nips03.ps.gz">Markov Models for Automated ECG Interval Analysis</A>.<br>
<i>Advances in Neural Information Processing Systems 16 (NIPS
2003)</i>.
<BR>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/nph_nips03.pdf">[pdf version]</A>
<br></li>


<li> L. C. Pickup, S. J. Roberts & A. Zissermann (2003). <br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/LCPickup_SuperRes.ps.gz">A Sampled Texture Prior for Image Super-resolution</A>.<br>
<i>Advances in Neural Information Processing Systems 16 (NIPS
2003)</i>.
<BR>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/LCPickup_SuperRes.pdf">[pdf version]</A>
<br></li>

<li> C. Orphanidou, S. J. Roberts & I. M. Moroz (2003).<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/orphanid03.pdf">Voice Morphing using the Generative Topographic Mapping</A>. (pdf only)<br>
<i>Proceedings, International Conference on Computer, Communication and Control Technologies 2003</i>, Volume I, 222-225.<br></li>

<li> R. A. Choudrey & S. J. Roberts (2003).<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/vbicahmm2_ICA2003.ps.gz">Learning Hierarchical Dynamics using Independent Component
Analysis</A>.<br>
<i>International Conference on Independent Component Analysis,
Nara, Japan.</i>, 797-802.<br></li>

<li> R. A. Choudrey & S. J. Roberts (2003).<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/vbicahmm_ICA2003.ps.gz">Bayesian ICA with Hidden Markov Model Sources</A>.<br>
<i>International Conference on Independent Component Analysis,
Nara, Japan.</i>, 809-814.<br></li>

<li> R. A. Choudrey & S. J. Roberts (2003).<br>
<A HREF="http://www.robots.ox.ac.uk/~parg/pubs/mixvbica_ICA2003.ps.gz">Variational Bayesian Mixture of Independent Component Analysers for
Finding Self-Similar Areas in Images</A>. <br>
<i>International Conference on Independent Component Analysis,
Nara, Japan.</i>, 107-112.<br></li>

<li> S. J. Roberts & R. A. Choudrey (2003).<br>
Independent Data Decomposition.<br>
<i>International Conference on Independent Component Analysis,
Nara, Japan.</i>, 451-456.<br></li>

<li>R. Choudrey & S. Roberts (2003).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/mix_vbica.ps.gz">
Variational Mixture of Bayesian Independent Component Analysers.</a>
<br> <i>Neural Computation</i> 15(1). <BR></li>

<li>S. Roberts & R. Choudrey (2003).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/ica_pos.ps.gz">
Data Decomposition using Independent Component Analysis with Prior Constraints.</a>
<br><i>Pattern Recognition</i> 36(8). <BR></li>

<li>I. Rezek, S. J. Roberts and P. Sykacek (2003).<br>
<a HREF="http://www.robots.ox.ac.uk/~parg/pubs/varchmm_aistats2002.pdf">
Ensemble Coupled Hidden Markov Models for Joint Characterisation of
Dynamic Signals.</a>
<br><i>Ninth International Workshop on Artificial Intelligence and
Statistics, 2003</i>.<BR></li>

<li>I. Rezek and S.J. Roberts (2003)<br>
<A href="pubs/linearbelprob.pdf">An Operator Interpretation of Message Passing</a>
<br>Technical Report <i>PARG-03-01</i>, 2003.
</li>

<li>Charles Fox (2003)
<A href="pubs/qcf.pdf">QCF: Quantum Computing Functions for MATLAB</a>
<br>User manual for software available from <a href="http://www.robots.ox.ac.uk/~charles/">Charles Fox</a>
<br>Technical Report <i>PARG-03-02</i>
</li>
</ul>


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</div>

<H3><A NAME="tag2002">2002</A></H3>
<ul>
<li>N. P. Hughes and D. Lowe (2002)
<br><a HREF="http://www.robots.ox.ac.uk/~nph/Pubs/nips02.ps.gz">Artefactual Structure from Least Squares Multidimensional
Scaling.</a>
*<br>Advances in Neural Information Processing Systems 15 (NIPS2002).**
<br><a HREF="http://www.robots.ox.ac.uk/~nph/Pubs/nips02.pdf">[pdf version]</a>
</li>

<li>P. Sykacek and S. Roberts (2002).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/nips02.ps.gz">Adaptive classification by variational Kalman filtering.</a>
*<br>Advances in Neural Information Processing Systems 15 (NIPS2002).**
</li>

<li>P. Sykacek, S. Roberts and M. Stokes (2002).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/albposter.pdf">Adaptive BCI based on variational Bayes: an empirical evaluation.</a> Albany BCI workshop, June 2002
(BEST TECHNICAL PAPER AWARD).

</li>

<li>S. Roberts & W. Penny (2002).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/vbj.ps.gz">Variational Bayes for Generalised Autoregressive Models.</a>
<br><i>IEEE Transactions on Signal Processing.</i>.
</li>

<li>W. Penny & S. Roberts (2002).
<br><a
HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/vbmar.ps.gz">Bayesian Multivariate Autoregressive Models
with Structured Priors.</a>
<br><i>IEE Proceedings on
Vision, Signal & Image Processing.</i> 149(1), 33-41.
</li>

<li>I. Rezek, M. Gibbs & S.J. Roberts, (2002).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/mapchmm_vlsi01.ps.gz"> Maximum a Posteriori Estimation of Coupled Hidden Markov Models.</a>
<br> <i>Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology</i> 32(1):55-66.
</li>

<li>I. Rezek and S.J. Roberts (2002).
<br><a href="http://www.robots.ox.ac.uk/~parg/pubs/varhmm_dsp2002.pdf">
Ensemble Hidden Markov Models for Biosignal Analysis. </a>
<br><i>14th International Conference on Digital Signal
Processing, Santorini, Greece, 2002</i>.
</li>

<li>L. C. Pickup & S. J. Roberts (2002).
<br><a HREF="http://www.robots.ox.ac.uk/~parg/pubs/lyndsey4yp.ps.gz">
Machine Learning Approaches For Brain-Computer Interfacing</a>
<br><i>Undergraduate Final Year Project</i>.

<br>Technical Report PARG-02-01, May 2002.
</li>

<li>J. Rittscher, A. Blake and S.J. Roberts (2002).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/motion.pdf"> Towards the Automatic Analysis of Complex Human Motions.</a>
<br><i>Image and Vision Computing Journal</i>.
</li>

<li>O. Ortolani, A. Conti, A. Di Filippo, C. Adembri, E. Moraldi, A. Evangelisti, M. Maggini and S. J. Roberts (2002). <BR>
EEG signal processing in anaesthesia. Use of a neural network technique for monitoring depth of anaesthesia.<BR>
<i>Br. J. Anaesth.</i> (2002) 88 (5): 644-648. doi: 10.1093/bja/88.5.644.
</li>
</ul>

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</div>

<H3><a NAME="tag2001">2001</a></H3>

<ul>
<li> E. Curran, P. Sykacek, M. Stokes, S. Roberts, W. Penny, I. Johnsrude, A. Owen (2001).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/allres.ps.gz">Cognitive Tasks
for driving a Brain Computer Interfacing System: a pilot study.</a>
<br>Technical Report PARG-01-07, March 2001.
</li>

<li>S. Roberts & R. Choudrey (2001).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/ica_comms.ps.gz">Detection
of Small Embedded Signals in Noise using Probabilistic Independent
Component Analysis.</a>
<br>Technical Report PARG-01-06.
</li>

<li>R. Choudrey & S. Roberts (2001).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/vbicaTR2001.ps.gz">Variational
Bayesian Independent Component Analysis with Flexible Sources.</a>
<br>Technical Report PARG-01-03.
</li>

<li>R. Choudrey & S. Roberts (2001).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/vbicaICA2001.ps.gz">
Flexible Bayesian Independent Component Analysis for Blind Source
Separation.</a>
<br><i>Proceedings of ICA-2001, San Diego,
December 2001.</i>
</li>

<li>P. Sykacek & S. Roberts (2001).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/nips01.ps.gz">Bayesian
time series classification.</a>
<br><i>Proceedings of NIPS
2001</i>.
</li>

<li>I. Rezek & S. Roberts (2001).
<br>Variational Inference for Hidden Markov Models.
<br>Technical Report PARG-01-02.
</li>

<li>S. Roberts, C. Holmes & D. Denison (2001).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/rjc_icann.ps.gz">Minimum
Entropy Data Clustering using RJ-MCMC.</a>
<br><i>Proceedings of
ICANN 2001, Vienna, August 2001</i>.

<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/rjc_icann.pdf">[PDF version]</a>
</li>

<li>S. Roberts & W. Penny (2001).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/mix_icann01.ps.gz">Mixtures
of Independent Component Analysers.</a>
<br> <i>Proceedings of ICANN 2001, Vienna, August 2001</i>.
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/mix_icann01.pdf">[PDF version]</a>
</li>

<li>P. Sykacek, S. Roberts, I. Rezek, A. Flexer & G. Dorffner
(2001).

<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/sleep_icann01.ps.gz"> A
probabilistic approach to high-resolution sleep analysis.</a>
<br><i>Proceedings of ICANN 2001, Vienna, August 2001</i>.
</li>

<li>S. Roberts & R. Everson (2001).
<br><a HREF="http://uk.cambridge.org/order/WebBook.asp?ISBN=0521792983">Independent Component Analysis: principles and practice.
<br></a> Cambridge University Press, March 2001.
</li>

<li>S. Roberts, C. Holmes & D. Denison (2001).

<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/rjclust_v2.ps.gz">Minimum Entropy data partitioning using
Reversible Jump Markov Chain Monte Carlo.</a>
<br><i>IEEE Transactions on Pattern Analysis & Machine Intelligence</i>,
Vol.23, 8, August 2001, 909-915.
</li>
</ul>
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</div>


<H3><A NAME="tag2000">2000</A></H3>
<ul>
<li>P. Sykacek, I. Rezek & S. Roberts (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/sensf.ps.gz">Markov chain
Monte Carlo methods for Bayesian sensor fusion.</a>
<br>Technical Report PARG-00-11, July 2000.
</li>

<li>S. Roberts (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/novelty_ext.ps.gz">Extreme Value Statistics for Novelty Detection in Biomedical Signal
Processing.</a>
<br><i>IEE Proceedings Science, Technology & Measurement.</i> Vol. 147, issue 6, p363-367.
</li>

<li>I. Rezek, P. Sykacek, S. J. Roberts (2000).<br>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/chmm_ieeproc00.pdf">
Learning Interaction Dynamics with Coupled Hidden Markov Models.</a><br>
IEE Special Issue Proceedings Science, Measurement and Technology, Vol. 147(6), pp. 345-350.
<br><br>
</li>

<li>S. Roberts, I. Rezek, R. Everson, H. Stone, S. Wilson & C. Alford (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/vigil_ext.ps.gz">Automated
assessment of Vigilance using committees of Radial Basis Function
Analysers.</a>
<br><i>IEE Proceedings Science, Technology &
Measurement.</i> Vol. 147, issue 6, p333-338.
</li>

<li>I. Rezek, P. Sykacek & S. Roberts (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/chmm_ext.ps.gz">A Comparison of Bayesian and Maximum
Likelihood Learning of Coupled Hidden Markov Models</a>
<br><i>IEE
Proceedings Science, Technology & Measurement.</i> Vol. 147, issue 6, p345-350.
</li>

<li>R. Choudrey, W. Penny & S. Roberts (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/final84.ps.gz">An ensemble learning approach to Independent
Component Analysis.</a>
<br><i>Proceedings of Neural Networks for
Signal Processing, Sydney, December 2000. </i>Technical Report
PARG-00-8, April 2000.
</li>

<li>S. Roberts, C. Holmes & D. Denison (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/rjclust.ps.gz">Minimum Entropy data partitioning using
Reversible Jump Markov Chain Monte Carlo.</a>
<br>Technical Report
PARG-00-7, April 2000.
</li>

<li>S. Roberts (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/novelty.ps.gz">Extreme Value
Statistics for Novelty Detection in Biomedical Signal Processing.</a>
<br><i>Proceedings of MEDSIP-2000, International Conference on Advances in
Medical Signal and Information Processing.</i>
<br><a HREF="Pubs/novelty.pdf">[pdf version]</a>
</li>

<li>I.Rezek, P. Sykacek & S. Roberts (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/chmm.ps.gz">Coupled hidden Markov models for biosignal
interaction modelling.</a>
<br><i>Proceedings of MEDSIP-2000,
International Conference on Advances in Medical Signal and Information
Processing.</i>
</li>

<li>W. Penny, S. Roberts & R. Everson (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/hmicab.ps.gz">Hidden Markov Independent Components Analysis
for biosignal analysis.</a>
<br><i>Proceedings of MEDSIP-2000,
International Conference on Advances in Medical Signal and Information
Processing.</i>

</li>

<li>S. Roberts, I. Rezek, R. Everson, H. Stone, S. Wilson & C. Alford (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/vigil1.ps.gz">Automated
assessment of Vigilance using committees of Radial Basis Function
Analysers.</a>
<br><i>Proceedings of MEDSIP-2000, International Conference
on Advances in Medical Signal and Information Processing.</i>
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/vigil1.pdf">[pdf version]</a>
</li>

<li>I. Rezek &  S. Roberts (2000).

<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/chmmnnsp00.ps.gz">Estimation of Coupled Hidden Markov
Models with application to Biosignal Interaction Modelling.</a>
<br><i>Proceedings of Neural Networks for Signal Processing, Sydney,
December 2000. </i>Technical report PARG-00-5, April 2000.
</li>

<li>W. Penny & S. Roberts (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/armog2000.ps.gz">Variational Bayes for non-Gaussian
autoregressive models.</a>
<br><i>Proceedings of Neural Networks for
Signal Processing, Sydney, December 2000. </i>Technical report
PARG-00-4, April 2000.
</li>

<li>W. Penny & S. Roberts (2000).

<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/vbar2000.ps.gz">Bayesian methods for autoregressive
models.</a>
<br><i>Proceedings of Neural Networks for Signal
Processing, Sydney, December 2000. </i>Technical report
PARG-00-3. April 2000.
</li>

<li>W. Penny & S. Roberts (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/vbmog.ps.gz">Variational Bayes for 1-dimensional mixture
models.</a>
<br>Technical report PARG-00-2, April 2000.
</li>

<li>W. Penny & S. Roberts (2000).

<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/var.ps.gz">Notes on variational learning.</a>
<br>Technical report PARG-00-1, April 2000.
</li>

<li>R. Dybowski & S. Roberts (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/rdsrnnerr.ps.gz">Confidence
Intervals and Prediction Intervals for Feed-Forward Neural
Networks.</a>
<br>In: Clinical Applications of Artificial Neural
Networks. Eds. R. Dybowski, V. Gant. Cambridge University Press.
</li>

<li>W. Penny, R. Everson & S. Roberts (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/hmica.ps.gz">Hidden
Markov Independent Components Analysis.</a>

<br>In: <i>Advances in
Independent Components Analysis</i>, (Ed M. Girolami), Kluwer Academic
Publishers.
</li>

<li>R. Everson & S. Roberts (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/pfica.ps.gz">Particle
Filters for Non-stationary ICA.</a>
<br>In: <i>Advances in Independent
Components Analysis</i>, (Ed M. Girolami), Kluwer Academic Publishers.
</li>

<li>S. Roberts & W. Penny (2000).

<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/rt_bci2.ps.gz">Real-time
Brain Computer Interfacing: a preliminary study using Bayesian
learning.</a>
<br><i>Medical & Biological Engineering and
Computing.</i>, Vol 38. num 1, pp 56-61.
<br><a
HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/rt_bci.pdf">[PDF version]</a>
</li>

<li>W. Penny, S. Roberts, E. Curran & M. Stokes (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/ieee.ps.gz">EEG-based
communication: a pattern recognition approach.</a>
<br><i>IEEE Transactions on Rehabilitation Engineering</i> 8(2), 214-216.

</li>

<li>S.J. Roberts, R. Everson, I. Rezek (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/entclust.ps.gz">Maximum
certainty data partitioning.</a>
<br><i>Pattern Recognition </i>33(5).
</li>

<li>R. Everson & S. Roberts (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/icachap.ps.gz">Independent
Components Analysis.</a>
<br>Technical report TR-99-8. May 1999. Draft of
chapter in <i>Artificial Neural Networks in Biomedicine, </i>Lisboa,
Ifeachor, Szczepaniak (Eds), Springer. Perspectives in Neural
Computing.

</li>

<li>W. Penny, D. Husmeier & S. Roberts (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/bayes.ps.gz">The Bayesian
Paradigm: Second Generation Neural Computing.</a>
<br>Technical report
TR-99-7. May 1999. Chapter in <i>Artificial Neural Networks in
Biomedicine, </i>Lisboa, Ifeachor, Szczepaniak (Eds),
Springer. Perspectives in Neural Computing.
</li>

<li>R.M. Everson & S.J. Roberts, (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/vlsi.ps.gz">Blind Source
Separation for Non-stationary Mixing.</a>

<br><i>Journal of VLSI
Signal Processing-Systems for Signal, Image, and Video Technology</i>,
26:8, 15-24, 2000.
</li>

<li>R. Everson and S. Roberts (2000).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/spectrum.ps.gz">Inferring
the eigenvalues of covariance matrices from limited, noisy data.</a>
<br>(Original Research report TR-98-11, Sept 1998). <i>IEEE transactions
on signal processing</i>, 48(7), 2083-2091.
</li>
</ul>
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</div>


<H3><a NAME="tag1999">1999</a></H3>

<ul>
<li> W.D. Penny and S.J. Roberts (1999).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/dynamic.ps.gz">Dynamic
models for nonstationary signal segmentation </a>
<br>Technical Report
TR-98-14. <i>Computers and Biomedical Research </i>32(6) pp. 483-502.
</li>

<li>S. Roberts, R. Everson, I.Rezek, P. Anderer & A. Schloegl (1999).

<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/embec99.ps.gz">Tracking
ICA for eye-movement artefact removal.</a>
<br><i>Proceedings of EMBEC-99</i>.
</li>

<li>I. Rezek, S. Roberts, P. Sykacek (1999).<br>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/sleepcomp_embec99.pdf">
Complexity Features for Sleep Stage Analysis.</a><br>
Proceedings of EMBEC, pages 1650-1651.
</li>

<li>P. Sykacek, S. Roberts, I. Rezek, A. Flexer & G. Dorffner (1999).<br>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/fsel_embec99.pdf">
Bayesian Wrappers Versus Conventional Filters: Feature Subset Selection In The SIESTA Project.</a><br>
Proceedings of EMBEC, pages 1652-1653.
</li>

<li>P. Sykacek, S. Roberts, I. Rezek, A. Flexer & G. Dorffner (1999).<br>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/bayref_embec99.pdf">
Reliability in Preprocessing - Bayes Rules SIESTA.</a><br>
Proceedings of EMBEC, pages 1656-1657.
</li>

<li>P. Sykacek, S. Roberts, I. Rezek, A. Flexer & G. Dorffner (1999).<br>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/semisupgmm_embec99.pdf">
Classification in the Sampling Paradigm: A Predictive Approach Towards a SIESTA Sleep Analyzer.</a><br>
Proceedings of EMBEC, pages 1660-1661.
</li>


<li>S.J. Roberts, R. Everson, I. Rezek (1999).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/minent_icann99.ps.gz">Minimum
Entropy Data Partitioning</a>
<br><i>ICANN-99, Proceedings of the
8th International Conference on Artificial Neural Networks , Springer
Verlag, Perspectives in Neural Computing, pp 844-849.</i>
</li>

<li>W. Penny, S. Roberts & M. Stokes (1999).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/ieee_bci.ps.gz">EEG-based
communication: a pattern recognition approach.</a>

<br>Technical report TR-99-6. May 1999.
</li>

<li>W. Penny & S. Roberts (1999).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/bci_ijcnn99.ps.gz">EEG-based
communication via dynamic neural network models.</a>
<br>Technical report
TR-99-5. May 1999. <i>Proceedings of International Joint
Conference on Neural Networks. IJCNN-99.</i>
</li>

<li>D. Husmeier & S. Roberts (1999).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/rbf_icann99.ps.gz">Regularisation
of RBF-networks with the Bayesian Evidence scheme.</a>

<br>Technical report TR-99-4. May 1999. <i>ICANN-99, Proceedings of the
8th International Conference on Artificial Neural</i>
<br><i>Networks, Springer Verlag, Perspectives in Neural Computing, pp533-538.</i>
</li>

<li>D. Husmeier, S. Roberts, G. Patton & M. McClure (1999).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/ks_ijcnn99.ps.gz">Neural
Networks for Predicting Kaposi's Sarcoma.</a>
<br>Technical report TR-99-3. May 1999. <i>Proceedings of International Joint
Conference on Neural Networks. IJCNN-99.</i>
</li>

<li>P.J. Wells, A. Wright & S.J. Roberts (1999).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/ieeacoust.ps.gz">Acoustic
Emission in Aerospace Structures.</a>
<br><i>Proceedings of IEE symposium
on condition monitoring</i>, April 1999.
</li>

<li>J.M. Spyers-Ashby, M.J. Stokes & S.J. Roberts
(1999).
<br>Classification of Normal and Pathological Tremors.
<br><i>Medical Engineering & Physics</i>, 21, 713-723.

</li>

<li>W.D. Penny, S.J. Roberts (1999)
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/dlr.ps.gz">Dynamic
Logistic Regression</a>
<br>In: <i>Proceedings IJCNN'99 </i>.
</li>

<li>W.D. Penny, S.J. Roberts (1999)
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/nlr.ps.gz">Nonstationary
Logistic Regression</a>
<br>Technical Report, Department of Electrical
Engineering, Imperial College.
</li>

<li>W. Penny, D. Husmeier & S.J. Roberts (1999).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/covw_icann.ps.gz">Covariance-based
weighting for optimal combination of model predictions.</a>
<br>Technical
Report TR-99-2. March 1999. <i>ICANN-99, Proceedings of the 8th
International Conference on Artificial Neural Networks, Springer
Verlag, Perspectives in Neural Computing, pp 826-831.</i>
</li>

<li>R. Everson & S.J. Roberts (1999).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/ns_ica.ps.gz">Non-stationary
Independent Component Analysis.</a>
<br>Technical Report TR-99-1. March
1999. <i>ICANN-99, Proceedings of the 8th International Conference on
Artificial Neural Networks, Springer Verlag, Perspectives in Neural
Computing, pp 503-508.</i>
</li>

<li>D. Husmeier, W.D. Penny, S.J. Roberts (1999).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/b_samp.ps.gz">An
Empirical Evaluation of Bayesian Sampling with Hybrid Monte Carlo for
Training Neural Network Classifiers.</a>
<br>TR-98-8 <i>Neural Networks</i>. 12, pp 677-705.
</li>

<li>W.D. Penny and S.J. Roberts (1999).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/evidence-tech.ps.gz">Bayesian
neural networks for classification: how useful is the evidence
framework?</a>
<br>TR-97-5 (1997). <i>Neural Networks</i>.12, pp 877-892.
</li>

<li>R. Everson and S. Roberts (1999).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/re_sr_ica.ps.gz">Independent
Component Analysis: A flexible non-linearity and decorrelating
manifold approach</a>.
<br>Research Report TR-98-3 March 1998.
Appears in <i>Neural Computation</i>. 11(8)
<br>A slightly shorter (more recent) version is <a
HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/ica.ps.gz">here.</a>
</li>
</ul>
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<H3><a NAME="tag1998">1998</a></H3>

<ul>
<li>W.D. Penny and S.J. Roberts (1998).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/hmm_obs.ps.gz">Hidden
Markov Models with Extended Observation Densities.</a>
<br>Technical
Report TR-98-15.
</li>

<li>W.D. Penny and S.J. Roberts (1998).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/kalman.ps.gz">Dynamic
Linear Models, Recursive Least Squares and Steepest Descent
Learning.</a>
<br>Technical Report TR-98-13.
</li>

<li>W.D. Penny and S.J. Roberts (1998).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/hmm_eeg.ps.gz">Gaussian
Observation Hidden Markov Models for EEG analysis.</a>

<br>Technical Report TR-98-12.
</li>

<li>I. Rezek and S.J. Roberts (1998).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/hilbert.ps.gz">Envelope
Extraction via Complex Homomorphic Filtering.</a>
<br>Research Report TR-98-9, June 1998.
</li>

<li>S.J. Roberts, I.A. Rezek, W.D. Penny, R.M. Everson (1998).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/iee_inf.ps.gz">The use of
advanced information processing methods in EEG analysis.</a>
<br>Proceedings of IEE colloquium on Intelligent Decision Support in Clinical
Practice, June 1998.
</li>

<li>D. Husmeier, W.D. Penny, S.J. Roberts
(1998).
<br>Emprical evaluation of Bayesian Sampling for Network
training.

<br>Research report TR-98-7, May 1998.
</li>

<li>W.D. Penny, D. Husmeier, S.J. Roberts (1998).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/ncovcomm.ps.gz">Covariance
based weighting for optimal combination of network
predictions.</a>
<br>Research report TR-98-6, May 1998.
<br>A slightly longer version is also available <a
HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/covcomm.ps.gz">here</a>.
</li>

<li>I. Rezek and S.J. Roberts (1998).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/dti.ps.gz">Causal Analysis with Information Flow.</a>
<br>Research Report TR-98-5.
</li>

<li>D. Husmeier, W.D. Penny, S.J. Roberts (1998).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/icann98.ps.gz">Empirical
Evaluation of Bayesian Sampling for Neural Classifiers.</a>
<br>In: L.Niklason,
M.Boden, T.Ziemke (Eds.), ICANN 98: Proceedings of the 8th International
Conference on Artificial Neural Networks, Springer Verlag, Perspectives
in Neural Computing, pp. 323-328.
</li>

<li>A. Alusi, S. Marchand-Maillet and S.J. Roberts
(1998).
<br>Ridge detection in the two-dimensional discrete
space.
<br>Research Report TR-98-4, March 1998.
</li>

<li>R. Everson and S. Roberts (1998).
<br><a HREF="http://www.ee.ic.ac.uk/research/neural/rme/nnsp98.ps.gz">Independent
Component Analysis: A flexible non-linearity and decorrelating
manifold approach.</a>
<br>In: Proceedings of IEEE conference on neural
networks and signal processing VIII, NNSP-98, Cambridge,
1998. pp33-42
</li>

<li>S.J. Roberts (1998).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/evt.ps.gz">Novelty
Detection using Extreme Value Statistics.</a>
<br>Research Report TR-98-2 (Revision 4.0 Sept 1998)
<br>Appears in <i>IEE Proceedings - Vision, Image
&amp; Signal Processing</i>, 146(3), pp 124-129.
</li>

<li>J.M. Spyers-Ashby, P. Bain & S.J. Roberts (1998).
<br>A comparison
of fast Fourier transform (FFT) and autoregressive (AR) spectral
estimation techniques for the analysis of tremor data.
<br><i>Journal of
Neuroscience Methods</i>, special issue <i>Wave form and Systems
Analysis</i>, Ed. Michael Gresty. 83, 35-43.

</li>

<li>W.D. Penny, S.J. Roberts and M.J. Stokes (1998).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/imag-tech2.ps.gz">Imagined
Hand Movements Identified from the EEG Mu-Rhythm.</a>
<br>Technical Report
TR-98-1.
</li>

<li>S.J. Roberts, W. Penny & I.Rezek (1998).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/bcipap.ps.gz">Temporal
and Spatial Complexity measures for EEG-based Brain-Computer
Interfacing.</a>
<br>(draft 3.6 January 1998, revised version 4.3 March
1998): <i>Medical & Biological Engineering & Computing</i>,
37(1), 93-99.

</li>

<li>S.J. Roberts (1998).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/ica_baye.ps.gz">Independent
Component Analysis: Source Assessment & Separation, a Bayesian
Approach.</a>
<br><i>IEE Proceedings - Vision, Image & Signal
Processing</i>, 145(3), 149-154, 1998
</li>

<li>T. Mouroutis, S.J. Roberts & A.A. Bharath (1998).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/cell_segment.ps.gz">Robust
Cell Nuclei Segmentation using Statistical Modelling.</a>

<i>Bioimaging</i>, Vol 6(2), pp 79-91, June 1998.
<br>Also available on-line via the IOP website, <a HREF="http://www.iop.org">www.iop.org</a>.
</li>

<li>I.A. Rezek and S.J. Roberts (1998).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/complexity.ps.gz">Stochastic
Complexity Measures for Physiological Signal Analysis.</a>
<br><i>IEEE Transactions on Biomedical Engineering</i>, Vol. 44, No 9, 1186-1191,
Sept. 1998
</li>
</ul>
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<H3><A NAME="tag1997">1997</A></H3>
<ul>
<li>I. Rezek, S. J. Roberts, A. Alusi, M. Arnold, H. Witte
(1997).
<br>Physiological State Identification in Feature
Space.
<br>Proceedings of the World Congress on Medical Physics and
Biomedical Engineering, Nice, France , p 565, September 14-19, 1997.
</li>

<li>A.H.Alusi, S.J.Roberts (1997).
<br>Structural Feature Extraction of Brain MRI Images
<br>Proceedings of the World Congress on Medical Physics and Biomedical
Engineering, Nice, France , p 725, September 14-19, 1997.
</li>

<li>I.A. Rezek & S.J. Roberts (1997).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/covclust.ps.gz">Unsupervised
Clustering using Metric Space Connectedness.</a>
<br>Research Report TR-97-4, June 1997.
</li>

<li>S.J. Roberts, I. Rezek & W. Penny (1997).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/el_gmm.ps.gz">Unsupervised
Data Partitioning: a Bayesian Approach.</a>
<br>Research Report TR-97-3, June 1997.
</li>

<li>S.J. Roberts, D. Husmeier, I. Rezek & W. Penny
(1997/1998).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/gmmclust.ps.gz">Bayesian
Approaches to Gaussian Mixture Modelling.</a>
<br>June 1997 (1.0), revised
version August 1998 (5.3). <i>IEEE Transactions on Pattern Analysis
and Machine Intelligence</i>, Vol. 20, No. 11, 1133-1142.
</li>

<li>Will Penny & Steve Roberts (1997).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/bayes-eeg.ps.gz">Bayesian
neural networks for detection of imagined finger movements from
single-trial EEG.</a>
<br>Research report TR-97-2, May 1997
</li>

<li>I.A. Rezek, S.J. Roberts (1997).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/iee_bio_sig97.ps.gz">Parametric
Model Order Estimation: A Brief Review.</a>
<br>IEE Colloquium on the Use of Model-based
Digital Signal Processing Techniques in the Analysis of Biomedical Signals,
April 1997.
</li>

<li>T. Mouroutis, S. J. Roberts, A. Bharath & G. Alusi (1997).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/dsp97.ps.gz">Compact
Hough Transform and Maximum Likelihood Approach to Cell Nuclei
Detection.</a>
<br>Proceedings of 13th International Conference on Digital
Signal Processing DSP'97, 2-4 July 1997, Santorini, Greece,
pp.869-872.
</li>

<li>W. Penny and S.J. Roberts (1997).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/nnerrors.ps.gz">Neural
network predictions with error bars.</a>

<br>Research Report TR-97-1, February
1997.
</li>

<li>S.J. Roberts and W. Penny (1997).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/e_lett.ps.gz">A Maximum
Certainty Approach to Feedforward Neural Networks.</a>
<br>Electronic Letters, 33 (4), 306-307.
</li>

<li>S.J. Roberts & W. Penny (1997).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/sensrev.ps.gz">Neural
Networks : Friends or Foes?</a>
<br>Sensor Review 17:1, 1997.
</li>

<li>S.J. Roberts (1997).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/cluster.ps.gz">Parametric
and Non-parametric Unsupervised Cluster Analysis.</a>
<br>Pattern Recognition, 30:2, 261-272.
</li>
</ul>

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<H3><A NAME="tag1996">1996</A></H3>
<ul>
<li>S.J. Roberts, W. Penny, D. Pillot (1996).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/ieefault.ps.gz">Novelty,
Confidence & Errors in Connectionist Systems.</a>
<br>Proceedings of IEE Colloquium on Intelligent Sensors and Fault Detection, September 1996,
1996/261 : 10/1-10/6

</li>

<li>S.J. Roberts & W. Penny (1996).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/errors.ps.gz">Novelty, Confidence & Errors in Connectionist Systems.</a>
<br>Research Report TR-96-1: October 1996.
</li>

<li>J. Pardey, S. Roberts, L. Tarassenko, J. Stradling (1996).<br>
A new approach to the analysis of the human sleep/wakefulness continuum.<BR>
Journal of Sleep Research, 5(4), 201-10.
</li>

<li>S.J. Roberts (1996).
<a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/icpr96_cam_ready.ps.gz">Scale-space
Cluster Analysis</a>
<br>Proceedings of ICPR-96, IEEE, Vol 2, pp 106-110,
August 1996.
</li>

<li>I. Matalas, S. Roberts & H. Hatzakis (1996).
<br>A set of multiresolution texture features suitable for unsupervised image
segmentation.
<br>Proceedings of EUSIPCO-96, September 1996, pp 1495-1498.
</li>

<li>H. Hatzakis, S.J. Roberts & I. Matalas (1996).

<br>Textural 3-Dimensional Multi-scale Analysis of MRI Volumes of the
Brain.
<br>Proceedings of EUSIPCO-96, September 1996, pp 363-366.
</li>

<li>J. Pardey, S. Roberts, L. Tarassenko, (1996).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/jmep.ps.gz">A Review of
Parametric Modelling Techniques for EEG Analysis.</a>
<br>Med. Eng. Phys.,
18(1), 2-11.
</li>

<li>S. J. Roberts (1996).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/iee_ai.ps.gz"> Assessing
the Confidence of Classification and Prediction in Artificial Neural
Networks.</a>
<br>Proceedings of IEE colloquium on AI methods in biosignal
analysis, April 1996, 1996/100 : 4/1-4/6.
</li>

<li>A. Outten, S. J. Roberts & M. Stokes (1996).
<br>Neural Network Analysis of Human Muscle Activity.
<br>Proceedings of IEE colloquium on AI methods in biosignal analysis, April 1996, 1996/100 : 7/1-7/6.
</li>

<li>M. Krkic, S. J. Roberts, I. Rezek & C. Jordan (1996).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/iee_ana.ps.gz">EEG-based
Assessment of Anaesthetic Depth using Neural Networks.</a>
<br>Proceedings of
IEE colloquium on AI methods in biosignal analysis, April 1996,
1996/100 : 10/1-10/6.
</li>
</ul>

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<H3><A NAME="tag1995">1995</A></H3>
<ul>
<li>S. J. Roberts, M. Krkic, I. Rezek, J. Pardey, L. Tarassenko,
J. Stradling & C. Jordan (1995)
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/iee_sleep.ps.gz">The use
of Neural Networks in EEG Analysis</a>
<br>Proceedings of IEE Colloquium on Sleep Analysis, December 1995.
</li>

<li>S. Roberts and L. Tarassenko, (1995).
<br>Automated EEG Analysis using an RBF Network.
<br>In: Applications of Neural Networks (A.F. Murray, ed.). Kluwer Academic Publishers, 305-322.
</li>

<li>L. Tarassenko, J. Pardey, S. Roberts, H. Chia & M. Laister (1995)
<br>Neural Network Analysis of Sleep Disorders
<br>Proceedings of ICANN 95, Paris, October 1995.
</li>

<li>D. K. Siegwart, L. Tarassenko, S. J. Roberts, J. R. Stradling & J. Partlett (1995)
<br>Sleep Apnoea Analysis from Neural Network Post-Processing

<br>Proceedings of IV International Conference on Artificial Neural Networks, Cambridge, June 1995, 427-432.
</li>
</ul>

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<H3><A NAME="tag1994">1994</A></H3>
<ul>
<li>L. Tarassenko and S. Roberts, (1994).
<br>Supervised and Unsupervised Learning in Radial Basis Function Classifiers.
<br>Invited paper : IEE Proceedings on Vision / Image & Signal Processing, 141(4), 210-216.

</li>

<li>S. Roberts and L. Tarassenko, (1994).
<br><a href="http://www.robots.ox.ac.uk/~sjrob/Pubs/robertsTarassenko_RAN_1994.pdf">A Probabilistic Resource Allocating Network for Novelty Detection.</a>
<br>Neural Computation 6 270-284.
</li>

<li>J. Pardey, S. Roberts, L. Tarassenko (1994)
<br>The Application of Artificial Neural Networks to Biomedical Signal Processing
<br>IEE Colloquium on the Application of Neural Networks to Signal Processing, London, December 1994.
</li>

<li>S. Roberts, T. Chaumeron and J. Magdolen (1994).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/eusipco-94.ps.gz">Adaptive Noise Removal from Complex Signals using the Wavelet Transform.</a>
<br>Proceedings of the VII European Signal Processing Conference, Edinburgh, September 1994, 66-69.
</li>

<li>S. Fredrickson, S. Roberts, N. Townsend and L. Tarassenko (1994).
<br>Speaker Identification using Networks of Radial Basis Functions.
<br>Proceedings of the VII European Signal Processing Conference (EUSIPCO-94), Edinburgh, September 1994, 812-815.
</li>

<li>S. Roberts, L. Tarassenko, J. Pardey and D. Siegwart (1994).
<br><a HREF="http://www.robots.ox.ac.uk/~sjrob/Pubs/valid.ps.gz">A Validation Index for Artificial Neural Networks.</a>
<br>Proceedings of First International Conference on Neural Networks and Expert Systems in
Medicine and Healthcare (NNESMED-94), Plymouth, August 1994, 23-30.
</li>
</ul>

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<H3><a NAME="tagtheses">Some recent D.Phil Theses</a></H3>
<ul>

<LI>Jaleh Zand (2022).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/jalehZandFinalThesis.pdf">
Multimodal Probabilistic Reasoning for Prediction and Coordination
Problems in Machine Learning</a><BR>

<LI>Shaan Desai (2022).<BR>
<a
href="http://www.robots.ox.ac.uk/~parg/pubs/theses/shaan_desai_pinns.pdf">
Physics-Informed Neural Networks for Data-Efficient Learning</a><BR>

<LI>Arno Blaas (2021). <BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/arno_blaas_final_thesis.pdf">On the Adversarial Robustness of
Bayesian Machine Learning Models</a><BR>

<LI>Matthew Willetts (2021). <BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/willetts_thesis_final.pdf">Robustness, Structure and Hierarchy in Deep Generative Models</a><BR>

<LI>Kyriakos Polymenakos (2020). <BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/kpol_final_thesis.pdf">Safe Model Based Policy Search
</a><BR>

<LI>Zihao Zhang (2020). <BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/zihao_zhang.pdf">Hierarchical Modelling for Financial Data
</a><BR>

<LI>Oliver Bent (2020). <BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/oliver_bent_thesis.pdf">Machine Learning applied to Prediction, Control and Planning from Dynamic Epidemiological Models
</a><BR>

<LI>Diego Granziol (2020). <BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/diego_final_thesis.pdf">
Spectral Machine Learning
</a><BR>

<LI>Ivan Kiskin (2020).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/IvanPhDThesisFinal.pdf">
Machine learning for acoustic mosquito detection
</a><BR>

<LI>Bryan Lim (2020). <BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/bryanLim.pdf">
Deep Learning for Time Series Prediction & Decision Making Over Time
</a><BR>

<LI>Richard Everett (2020).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/richardEverett_2020.pdf">
Strategically Training and Evaluating Agents in Procedurally Generated Environments
</a><BR>

<LI>Adam Cobb (2020).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/adam_cobb_thesis_final.pdf">
The Practicalities of Scaling Bayesian Neural Networks to Real-World Applications
</a><BR>

<LI>Bernardo Perez Orozco (2020).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/bernardo_orozco.pdf">
Recurrent Neural Networks for Time Series Prediction
</a><BR>

<LI>Mark McLeod (2019).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/mark_mcleod_final_thesis.pdf">
Optimizing Bayesian Optimization
</a><BR>

<LI>Ahsan Alvi (2019).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/Ahsan_Alvi_Thesis_final.pdf">
Practical Bayesian optimisation for hyperparameter tuning
</a><BR>

<LI>Babak Damghani (2019).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/bDamghani.pdf">
Data-Driven Models & Mathematical Finance: Apposition or Opposition?
</a><BR>

<LI>Jonny Downing (2019).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/jonny_downing.pdf">
Model-based decision support with uncertain human-centric data
</a><BR>

<LI>Jack FitzSimons (2019).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/KernelMethods_JKFitzsimons.pdf">
Kernel Methods: Generalisations, Scalability and Towards the Future of Machine Learning
</a><BR>

<LI>Sid Ghoshal (2019).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/sid_ghoshal.pdf">
Algorithmic Decision Making in Financial Markets
</a><BR>

<LI>Favour Nyikosa (2018).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/FavourMandanjiNyikosaThesis.pdf">
Adaptive Bayesian Optimization for Dynamic Problems
</a><BR>

<LI>Ali Asad (2018).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/ali_asad_thesis_2018.pdf">
Analysis of Financial Time Series using Non-Parametric Bayesian Techniques
</a><BR>

<LI>Chris Lloyd (2018).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/lloyd_thesis_website.pdf">
Probabilistic Inference for Dynamic Networks and Complex Event Processes
</a><BR>

<LI>Elmarie van Heerden (2017).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/elmarieVanHeerden2017.pdf">
Data Challenges In Pulsar Searches</a><BR>

<LI>Ibrahim Almosallam (2017).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/ibrahim_almosallam_thesis.pdf">
Heteroscedastic Gaussian Processes for Uncertain and Incomplete Data</a><BR>

<LI>Yves-Laurent Kom-Samo (2017).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/yvesLaurentKomSamo_thesis.pdf">
Advances in Kernel Methods Towards General-Purpose and Scalable Models</a><BR>

<LI>Tom Gunter (2017).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/tom_gunter_thesis.pdf">
Towards Efficient Bayesian Inference: Cox Processes and Probabilistic Integration</a><BR>

<LI>Rob McInerney (2014).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/robMcInerney2014.pdf">
Decision Making under Uncertainty</a><BR>

<LI>Jan-Peter Calliess (2014).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/janCalliess_thesis.pdf">
Conservative decision-making and inference in uncertain dynamical systems</a><BR>

<LI>Nabeel Gillani (2014).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/nabeel_gillani_thesis.pdf">
Uncovering latent features in massive open online courses</a><BR>

<LI>Mark Smith (2014).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/markSmithMScThesis2014.pdf">
Anomaly Detection in Vessel Track Data</a><BR>

<LI>Edwin Simpson (2014).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/thesis_ed_simpson.pdf">
Combined Decision Making with Multiple Agents</a><BR>

<LI>Ioannis Psorakis (2013).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/psorakis_thesis.pdf">
Probabilistic inference in ecological networks; graph discovery, community detection and modelling dynamic sociality</a><BR>

<LI>Nauman Shah (2013).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/nauman_shah_2013.pdf">
Statistical Dynamical Models of Multivariate Financial Time Series</a><BR>

<LI>Evangelos Roussos (2012).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/evangelos_Roussos_thesis.pdf">
Bayesian Methods for Sparse Data Decomposition and Blind Source Separation</a><BR>

<LI>Maike Kaufman (2010).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/MaikeKaufmann_thesis.pdf">
Local Decision-Making in Multi-Agent Systems</a><BR>

<LI>Roman Garnett (2010).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/RomanGarnett_thesis.pdf">
Learning from Data Streams with Concept Drift</a><BR>

<LI>Mike Osborne (2010).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/MichaelOsborne_thesis.pdf">
Bayesian Gaussian Processes for Sequential Prediction, Optimisation and Quadrature</a><BR>

<LI>Richard Mann (2009).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/RichardMann_thesis.pdf">
Prediction of Homing Pigeon Flight Paths using Gaussian Processes</a><BR>

<LI>Will Addison (2009).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/WillAddison_thesis.pdf">
Blind Source Separation using Spatial and Temporal Priors</a><BR>

<LI>Min Lee (2009).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/MinLee_thesis.pdf">
Sequential Forecasting and Decision Making in Dynamic and Incomplete Environments</a><BR>

<LI>Rob Freeman (2008).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/RobinFreeman_thesis.pdf">
Analysis of Avian Navigation</a><BR>

<LI>Charles Fox (2008).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/CharlesFox_thesis.pdf">
An Entangled Bayesian Gestalt: Mean-field, Monte-Carlo and Quantum Inference in Hierarchical Perception</a><BR>

<LI>Karen Lees (2008).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/KarenLees_thesis.pdf">
Data Projections for the Analysis and Visualisation of Bioinformatics Data</a><BR>

<li>Lyndsey Pickup (2007).<br>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/LyndseyPickup_thesis.pdf">
Machine Learning in Multi Frame Image Super-resolution.
</a><br>
</li>

<LI>Max Little (2006).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/MaxLittle_thesis.pdf">
Biomechanically Informed Nonlinear Speech Signal Processing</a><BR>

<LI>Sach Mukherjee (2005).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/SachiMukherjee_thesis.pdf">
Data-adaptive Test Statistics for Gene Expression Arrays</a><BR>

<LI>Rizwan Choudrey (2002).<BR>
<a href="http://www.robots.ox.ac.uk/~parg/pubs/theses/RizwanChoudrey_thesis.pdf">
Variational Methods for Bayesian Independent Component Analysis</a><BR>
<BR><BR>

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