Peter Dayan, a British computer and neuro scienctist, professor of computational neuroscience at University College London, and director of UCL's Gatsby Computational Neuroscience Unit [1] [2]. He obtained a B.Sc. in mathematics from University of Cambridge and a Ph.D. in artificial intelligence from University of Edinburgh, which focused on Bayesian network and neural network models of machine learning. He was postdoctoral researcher at the Salk Institute for Biological Studies working with Terrence J. Sejnowski, and the University of Toronto with Geoffrey E. Hinton, and assistant professor at MIT before relocating to Gatsby Computational Neuroscience Unit at UCL in 1998. Peter Dayan's work has been influential in several fields impinging on cognitive science, including machine learning, mathematical statistics, neuroscience and psychology - he has articulated a view in which neural computation is akin to a Bayesian inference process [3]. His research centers around self-supervised learning, reinforcement learning, temporal difference learning and population coding. He researched and published on Q-learning with Chris Watkins [4], and provided a proof of convergence of TD(λ) for arbitrary λ [5]. Along with Nicol N. Schraudolph and Terrence J. Sejnowski, he published on temporal difference learning to evaluate positions in Go [6].
Peter Dayan [7]

Selected Publications

1990 ...

• Peter Dayan (1990). Navigating Through Temporal Difference. NIPS 1990, pdf
• Chris Watkins, Peter Dayan (1992). Q-learning. Machine Learning, Vol. 8, No. 2
• Peter Dayan (1992). The convergence of TD (λ) for general λ. Machine Learning, Vol. 8, No. 3
• Peter Dayan, Geoffrey E. Hinton (1992). Feudal reinforcement learning. NIPS 1992, pdf
• Peter Dayan (1993). Improving generalisation for temporal difference learning: The successor representation. Neural Computation, Vol. 5, pdf
• Nicol N. Schraudolph, Peter Dayan, Terrence J. Sejnowski (1994). Temporal Difference Learning of Position Evaluation in the Game of Go. Advances in Neural Information Processing Systems 6
• Peter Dayan, Terrence J. Sejnowski (1994). TD(λ) converges with Probability 1. Machine Learning, Vol. 14, No. 1, pdf
• Peter Dayan, Terrence J. Sejnowski (1996). Exploration Bonuses and Dual Control. Machine Learning, Vol. 25, No. 1, pdf
• Peter Dayan (1999). Recurrent Sampling Models for the Helmholtz Machine. Neural Computation, Vol. 11, No. 3, pdf ^[9]

2000 ...

• Nicol N. Schraudolph, Peter Dayan, Terrence J. Sejnowski (2001). Learning to Evaluate Go Positions via Temporal Difference Methods. in Norio Baba, Lakhmi C. Jain (eds.) (2001). Computational Intelligence in Games, Studies in Fuzziness and Soft Computing. Physica-Verlag
• Peter Dayan, Laurence F. Abbott (2001, 2005). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press
• Peter Dayan (2008). Load and Attentional Bayes. NIPS 2008, pdf

2010 ...

• Peter Dayan (2012). How to set the switches on this thing. Current Opinion in Neurobiology, Vol. 22, pdf
• Arthur Guez, David Silver, Peter Dayan (2012). Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search. NIPS 2012, pdf
• Arthur Guez, David Silver, Peter Dayan (2013). Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search. Journal of Artificial Intelligence Research, Vol. 48, pdf
• Arthur Guez, David Silver, Peter Dayan (2014). Better Optimism By Bayes: Adaptive Planning with Rich Models. arXiv:1402.1958v1
• Arthur Guez, Nicolas Heess, David Silver, Peter Dayan (2014). Bayes-Adaptive Simulation-based Search with Value Function Approximation. NIPS 2014, pdf

External Links

• Gatsby Computational Neuroscience Unit | Professor Peter Dayan
• Research Biography of Peter Dayan | The David E. Rumelhart Prize 2012
• Peter Dayan from Wikipedia

References

1. ^ Gatsby Computational Neuroscience
2. ^ Gatsby Computational Neuroscience Unit | Professor Peter Dayan
3. ^ Research Biography of Peter Dayan | The David E. Rumelhart Prize 2012
4. ^ Christopher J. C. H. Watkins, Peter Dayan (1992). Q-learning. Machine Learning, Vol. 8, No. 2
5. ^ Peter Dayan (1992). The convergence of TD (λ) for general λ. Machine Learning, Vol. 8, No. 3
6. ^ Nicol N. Schraudolph, Peter Dayan, Terrence J. Sejnowski (1994). Temporal Difference Learning of Position Evaluation in the Game of Go. Advances in Neural Information Processing Systems 6
7. ^ Research Biography of Peter Dayan | The David E. Rumelhart Prize 2012
8. ^ DBLP: Peter Dayan
9. ^ Helmholtz machine from Wikipedia

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