Publikationen von P Dayan

Éltetö, N.; Dayan, P.: Habits of Mind: Reusing Action Sequences for Efficient Planning. 45th Annual Meeting of the Cognitive Science Society (CogSci 2023): Workshop "Compositionality in minds, brains and machines: a unifying goal that cuts across cognitive sciences", Sydney, Australia, 26. Juli 2023 - 29. Juli 2023. Proceedings of the Annual Meeting of the Cognitive Science Society 45, S. 195 - 201 (2023)

Rubino, V.; Hamidi, M.; Dayan, P.; Wu, C.: Compositionality under time pressure. 45th Annual Meeting of the Cognitive Science Society (CogSci 2023), Sydney, Australia, 26. Juli 2023 - 29. Juli 2023. Proceedings of the Annual Meeting of the Cognitive Science Society 45, S. 678 - 685 (2023)

Schulz, L.; Alon, N.; Rosenschein, J.; Dayan, P.: Emergent deception and skepticism via theory of mind. In: ICML 2023: First Workshop on Theory of Mind in Communicating Agents (ToM 2023). ICML 2023: First Workshop on Theory of Mind in Communicating Agents (ToM 2023), Honolulu, HI,USA, 28. Juli 2023. (eingereicht)

Alon, N.; Schulz, L.; Rosenschein, J.; Dayan, P.: A (dis-)information theory of revealed and unrevealed preferences. In: Information-Theoretic Principles in Cognitive Systems: Workshop at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022). NeurIPS 2022 Workshop on Information-Theoretic Principles in Cognitive Systems, New Orleans, LA, USA, 03. Dezember 2022. (2022)

Bruijns, S.; The International Brain Laboratory; Dayan, P.: Understanding Learning Trajectories With Infinite Hidden Markov Models. In: 2022 Conference on Cognitive Computational Neuroscience, P-1.19, S. 64 - 66. Conference on Cognitive Computational Neuroscience (CCN 2022), San Francisco, CA, USA, 25. August 2022 - 28. August 2022. (2022)

Khajehnejad, M.; Habibollahi, F.; Nock, R.; Arabzadeh, E.; Dayan, P.; Dezfouli, A.: Neural Network Poisson Models for Behavioural and Neural Spike Train Data. In: International Conference on Machine Learning, 17-23 July 2022, Baltimore, Maryland, USA, S. 10974 - 10996 (Hg. Chaudhuri, K.; Jegelka, S.; Song, L.; Szepesvari, C.; Niu, G. et al.). Thirty-ninth International Conference on Machine Learning (ICML 2022), Baltimore, MD, USA, 17. Juli 2022 - 23. Juli 2022. (2022)

Renz, F.; Grossman, S.; Dayan, P.; Doeller, C.; Schuck, N.: Representation learning facilitates different levels of generalization. In: 2022 Conference on Cognitive Computational Neuroscience, P-2.66, S. 460 - 462. Conference on Cognitive Computational Neuroscience (CCN 2022), San Francisco, CA, USA, 25. August 2022 - 28. August 2022. (2022)

Bröker, F.; Roads, B.; Dayan, P.; Love, B.: Teaching categories to human semi-supervised learners. In: 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2022), 2.8, S. 122 - 125. 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2022), Providence, RI, USA, 08. Juni 2022 - 11. Juni 2022. (2022)

Dayan, P.; Gagne, C.: Two steps to risk sensitivity. In: Advances in Neural Information Processing Systems 34: 35th Conference on Neural Information Processing Systems (NeurIPS 2021), S. 22209 - 22220 (Hg. Ranzato, M.; Beygelzimer, A.; Liang, P.; Vaughan, J.; Dauphin, Y.). Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), 06. Dezember 2021 - 14. Dezember 2021. Curran, Red Hook, NY, USA (2022)

Wise, T.; Charpentier, C.; Dayan, P.; Mobbs, D.: Modeling the mind of a predator: Interactive cognitive maps enable avoidance of dynamic threats. In: 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2022), 1.49, S. 32 - 33. 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2022), Providence, RI, USA, 08. Juni 2022 - 11. Juni 2022. (2022)

Gagne, C.; Dayan, P.: Catastrophe, Compounding & Consistency in Choice. In: Workshop on Human and Machine Decisions @ NeurIPS 2021 (WHMD 2021). Workshop on Human and Machine Decisions @ NeurIPS 2021 (WHMD 2021), 14. Dezember 2021. (2021)

Tano, P.; Dayan, P.; Pouget, A.: A local temporal difference code for distributional reinforcement learning. In: Advances in Neural Information Processing Systems 33, S. 13662 - 13673 (Hg. Larochelle, H.; Ranzato, M.; Hadsell, R.; Balcan, M.-F.; Lin, H.-T.). 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 06. Dezember 2020 - 12. Dezember 2020. Curran, Red Hook, NY, USA (2021)

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, 03. Mai 2021 - 07. Mai 2021. (2021)

Sezener, E.; Dayan, P.: Static and Dynamic Values of Computation in MCTS. 36th Conference on Uncertainty in Artificial Intelligence (UAI 2020), 03. August 2020 - 06. August 2020. Proceedings of Machine Learning Research (PMLR) 124, 26, S. 31 - 40 (2020)

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), S. e5 - e10 (2020)

Dezfouli, A.; Ashtiani, H.; Ghattas, O.; Nock, R.; Dayan, P.; Ong, C.: Disentangled behavioural representations. In: Advances in Neural Information Processing Systems 32, S. 2243 - 2252 (Hg. 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, 09. Dezember 2019 - 13. Dezember 2019. Curran, Red Hook, NY, USA (2020)

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, S. 826 - 829. Conference on Cognitive Computational Neuroscience (CCN 2019), Berlin, Germany, 13. September 2019 - 16. September 2019. (2019)

Dezfouli, A.; Morris, R.; Ramos, F.; Dayan, P.; Balleine, B.: Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models. In: Advances in Neural Information Processing Systems 31, S. 4233 - 4242 (Hg. Bengio, S.; Wallach, H.; Larochelle, H.; Grauman, K.; Cesa-Bianchi, N. et al.). 32nd Annual Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada, 03. Dezember 2018 - 08. Dezember 2018. Curran, Red Hook, NY, USA (2019)

Ahilan, S.; Dayan, P.: Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning. In: Annual Conference of the American Library Association (ALA 2019), 5, S. 1 - 5. Annual Conference of the American Library Association (ALA 2019) , Washington, DC, USA, 20. Juni 2019 - 25. Juni 2019. (2019)

Ahilan, S.; Dayan, P.: Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning. In: Workshop on Structure & Priors in Reinforcement Learning (SPiRL 2019) at ICLR 2019, S. 1 - 11. Workshop on Structure & Priors in Reinforcement Learning (SPiRL 2019) at ICLR 2019, New Orleans, LA, USA, 06. Mai 2019. (2019)