Publications of P Dayan

Book Chapter (13)

Book Chapter
Dayan, P.; Nakahara, H.: Models and Methods for Reinforcement Learning. In: Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience, Vol. 5: Methodology, 4. Ed., pp. 1 - 40 (Eds. Wixted, J.; Wagenmakers, E.-J.). Wiley, Hoboken, NJ, USA (2018)
Book Chapter
Dayan, P.: Exploration from Generalization Mediated by Multiple Controllers. In: Intrinsically Motivated Learning in Natural and Artificial Systems, pp. 73 - 91 (Eds. Baldassarre, G.; Mirolli, M.). Springer, Berlin, Germany (2013)
Book Chapter
Dayan, P.: Models of Value and Choice. In: Neuroscience of Preference and Choice: Cognitive and Neural Mechanisms, 2, pp. 33 - 52 (Eds. Dolan, R.; Sharot, T.). Elsevier/Academic Press, London, UK (2012)
Book Chapter
Daw, N.; Courville, A.; Dayan, P.: Semi-rational models of conditioning: The case of trial order. In: The Probabilistic Mind: Prospects for Bayesian cognitive science$, 19, pp. 427 - 448 (Eds. Chater, N.; Oaksford, M.). Oxford University Press, Oxford, UK (2008)
Book Chapter
Platt, M.; Dayan, P.; Dehaene, S.; McCabe, H.; Menzel, R.; Phelps, E.; Plassmann, H.; Ratcliff, R.; Shadlen, M.; Singer, W.: Neuronal Correlates of Decision Making. In: Better than conscious?: decision making, the human mind, and implications for institutions, 6 (Eds. Engel, C.; Singer, W.). MIT Press, Cambridge, MA, USA (2008)
Book Chapter
Daw, N.; Niv, Y.; Dayan, P.: Actions, policies, values, and the basal ganglia. In: Recent Breakthroughs in Basal Ganglia Research, 8, pp. 91 - 106 (Ed. Bezard, E.). Nova Science Publishers, Hauppauge, NY, USA (2006)
Book Chapter
Dayan, P.: Levels of Analysis in Neural Modeling. In: Encyclopedia of Cognitive Science, 2 Ed. (Ed. Nadel, L.). Wiley, Chichester, UK (2005)
Book Chapter
Dayan, P.; Watkins, C.: Reinforcement Learning. In: Encyclopedia of Cognitive Science, Vol. 3 (Ed. Nadel, L.). Wiley, Chichester, UK (2005)
Book Chapter
Dayan, P.: Helmholtz machines and wake-sleep learning. In: The Handbook of Brain Theory and Neural Networks, 2. Ed., pp. 522 - 524 (Ed. Arbib, M.). MIT Press, Cambridge, MA, USA (2003)
Book Chapter
Schraudolph, N.; Dayan, P.; Sejnowski, T.: Learning to Evaluate Go Positions via Temporal Difference Methods. In: Computational Intelligence in Games, pp. 77 - 98 (Eds. Baba, N.; Jain, L.). Physica Verlag, Heidelberg, Germany (2001)
Book Chapter
Dayan, P.: Unsupervised Learning. In: The MIT Encyclopedia of the Cognitive Sciences (Eds. Wilson, R.; Keil, F.). MIT Press, Cambridge, MA, USA (1999)
Book Chapter
Montague, P.; Dayan, P.: Neurobiological Modeling: Squeezing Top Down to Meet Bottom Up. In: A Companion to Cognitive Science, pp. 526 - 542 (Eds. Bechtel, W.; Graham, G.). Blackwell, Malden, MA, USA (1998)
Book Chapter
Oberlander, J.; Dayan, P.: Altered states and virtual beliefs. In: Connectionism, Concepts and Folk Psychology: The Legacy of Alan Turing Vol. 2, pp. 101 - 114 (Eds. Millican, P.; Clark, A.). Clarendon Press, Oxford, UK (1996)

Proceedings (1)

Exploration and Curiosity in Robot Learning and Inference (Dagstuhl Reports, 1). Dagstuhl Seminar 11131, Dagstuhl, Germany, March 27, 2011 - April 01, 2011. (2011)

Conference Paper (80)

Conference Paper
Tano, P.; Dayan, P.; Pouget, A.: A local temporal difference code for distributional reinforcement learning. In: Advances in Neural Information Processing Systems 33, pp. 13662 - 13673 (Eds. Larochelle, H.; Ranzato, M.; Hadsell, R.; Balcan, M.-F.; Lin, H.-T.). 34th Conference on Neural Information Processing Systems (NeurIPS 2020), December 06, 2020 - December 12, 2020. Curran, Red Hook, NY, USA (2021)
Conference Paper
Ahilan, S.; Dayan, P.: Correcting Experience Replay for Multi-Agent Communication. In: Ninth International Conference on Learning Representations (ICLR 2021). Ninth International Conference on Learning Representations (ICLR 2021), Vienna, Austria, May 03, 2021 - May 07, 2021. (2021)
Conference Paper
Sezener, E.; Dayan, P.: Static and Dynamic Values of Computation in MCTS. 36th Conference on Uncertainty in Artificial Intelligence (UAI 2020), August 03, 2020 - August 06, 2020. Proceedings of Machine Learning Research (PMLR) 124, 26, pp. 31 - 40 (2020)
Conference Paper
Browning, M.; Carter, C.; Chatham, C.; Den Ouden, H.; Gillan, C.; Baker, J.; Chekroud, A.; Cools, R.; Dayan, P.; Gold, J. et al.; Goldstein, R.; Hartley, C.; Kepecs, A.; Lawson, R.; Mourao-Miranda, J.; Phillips, M.; Pizzagalli, D.; Powers, A.; Rindskopf, D.; Roiser, J.; Schmack, K.; Schiller, D.; Sebold, M.; Stephan, K.; Frank, M.; Huys, Q.; Paulus, M.: Realizing the Clinical Potential of Computational Psychiatry: Report from the Banbury Center Meeting, February 2019. Banbury Center Meeting 2019, Huntington, NY, USA, 2019-02. Biological Psychiatry 88 (2), pp. e5 - e10 (2020)
Conference Paper
Dezfouli, A.; Ashtiani, H.; Ghattas, O.; Nock, R.; Dayan, P.; Ong, C.: Disentangled behavioural representations. In: Advances in Neural Information Processing Systems 32, pp. 2243 - 2252 (Eds. Wallach, H.; Larochelle, H.; Beygelzimer , A.; d'Alché-Buc, F.; Fox, E. et al.). Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, December 09, 2019 - December 13, 2019. Curran, Red Hook, NY, USA (2020)
Conference Paper
Jain, Y.; Gupta, S.; Rakesh, V.; Dayan, P.; Callaway, F.; Lieder, F.: How do people learn how to plan? In: Conference on Cognitive Computational Neuroscience (CCN 2019), PS-2A.70, pp. 826 - 829. Conference on Cognitive Computational Neuroscience (CCN 2019), Berlin, Germany, September 13, 2019 - September 16, 2019. (2019)
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