Satinder Singh

309 papers A* 97A 26B 2C 2Misc 4Journal 112Unranked 65

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Agency Is Frame-Dependent. CoRR David Abel, André Barreto, Michael Bowling, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh
2025	J	`jnl`	Capacity-Constrained Continual Learning. CoRR Zheng Wen, Doina Precup, Benjamin Van Roy, Satinder Singh
2025	J	`jnl`	Code World Models for General Game Playing. CoRR Wolfgang Lehrach, Daniel Hennes, Miguel Lázaro-Gredilla, Xinghua Lou, Carter Wendelken, Zun Li, Antoine Dedieu, Jordi Grau-Moya, Marc Lanctot, Atil Iscen, John Schultz, Marcus Chiam, Ian Gemp, Piotr Zielinski, Satinder Singh, Kevin P. Murphy
2025	J	`jnl`	Discovering state-of-the-art reinforcement learning algorithms. Nat. Junhyuk Oh, Gregory Farquhar, Iurii Kemaev, Dan A. Calian, Matteo Hessel, Luisa M. Zintgraf, Satinder Singh, Hado van Hasselt, David Silver
2025	J	`jnl`	Evaluating In Silico Creativity: An Expert Review of AI Chess Compositions. CoRR Vivek Veeriah, Federico Barbero, Marcus Chiam, Xidong Feng, Michael Dennis, Ryan Pachauri, Thomas Tumiel, Johan Obando-Ceron, Jiaxin Shi, Shaobo Hou, Satinder Singh, Nenad Tomasev, Tom Zahavy
2025	J	`jnl`	Generating Creative Chess Puzzles. CoRR Xidong Feng, Vivek Veeriah, Marcus Chiam, Michael Dennis, Ryan Pachauri, Thomas Tumiel, Federico Barbero, Johan Obando-Ceron, Jiaxin Shi, Satinder Singh, Shaobo Hou, Nenad Tomasev, Tom Zahavy
2025	A*	`conf`	Mastering Board Games by External and Internal Planning with Language Models. ICML John Schultz, Jakub Adámek, Matej Jusup, Marc Lanctot, Michael Kaisers, Sarah Perrin, Daniel Hennes, Jeremy Shar, Cannada A. Lewis, Anian Ruoss, Tom Zahavy, Petar Velickovic, Laurel Prince, Satinder Singh, Eric Malmi, Nenad Tomasev
2025	J	`jnl`	Plasticity as the Mirror of Empowerment. CoRR David Abel, Michael Bowling, André Barreto, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh
2024	A*	`conf`	Genie: Generative Interactive Environments. ICML Jake Bruce, Michael D. Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal M. P. Behbahani, Stephanie C. Y. Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott E. Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktäschel
2024	J	`jnl`	Genie: Generative Interactive Environments. CoRR Jake Bruce, Michael Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal M. P. Behbahani, Stephanie Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott E. Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktäschel
2024	J	`jnl`	Mastering Board Games by External and Internal Planning with Language Models. CoRR John Schultz, Jakub Adámek, Matej Jusup, Marc Lanctot, Michael Kaisers, Sarah Perrin, Daniel Hennes, Jeremy Shar, Cannada A. Lewis, Anian Ruoss, Tom Zahavy, Petar Velickovic, Laurel Prince, Satinder Singh, Eric Malmi, Nenad Tomasev
2023	A*	`conf`	A Definition of Continual Reinforcement Learning. NeurIPS David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado Philip van Hasselt, Satinder Singh
2023	J	`jnl`	A Definition of Continual Reinforcement Learning. CoRR David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado van Hasselt, Satinder Singh
2023	A*	`conf`	Combining Behaviors with the Successor Features Keyboard. NeurIPS Wilka Carvalho, Andre Saraiva, Angelos Filos, Andrew K. Lampinen, Loic Matthey, Richard L. Lewis, Honglak Lee, Satinder Singh, Danilo Jimenez Rezende, Daniel Zoran
2023	J	`jnl`	Combining Behaviors with the Successor Features Keyboard. CoRR Wilka Carvalho, Andre Saraiva, Angelos Filos, Andrew Kyle Lampinen, Loic Matthey, Richard L. Lewis, Honglak Lee, Satinder Singh, Danilo J. Rezende, Daniel Zoran
2023	A*	`conf`	Composing Task Knowledge With Modular Successor Feature Approximators. ICLR Wilka Carvalho, Angelos Filos, Richard L. Lewis, Honglak Lee, Satinder Singh
2023	J	`jnl`	Composing Task Knowledge with Modular Successor Feature Approximators. CoRR Wilka Carvalho, Angelos Filos, Richard L. Lewis, Honglak Lee, Satinder Singh
2023	—	`conf`	Discovering Evolution Strategies via Meta-Black-Box Optimization. GECCO Companion Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dalibard, Chris Lu, Satinder Singh, Sebastian Flennerhag
2023	A*	`conf`	Discovering Evolution Strategies via Meta-Black-Box Optimization. ICLR Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dalibard, Chris Lu, Satinder Singh, Sebastian Flennerhag
2023	A*	`conf`	Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality. ICLR Tom Zahavy, Yannick Schroecker, Feryal M. P. Behbahani, Kate Baumli, Sebastian Flennerhag, Shaobo Hou, Satinder Singh
2023	J	`jnl`	Diversifying AI: Towards Creative Chess with AlphaZero. CoRR Tom Zahavy, Vivek Veeriah, Shaobo Hou, Kevin Waugh, Matthew Lai, Edouard Leurent, Nenad Tomasev, Lisa Schut, Demis Hassabis, Satinder Singh
2023	J	`jnl`	Hierarchical Reinforcement Learning in Complex 3D Environments. CoRR Bernardo Ávila Pires, Feryal M. P. Behbahani, Hubert Soyer, Kyriacos Nikiforou, Thomas Keck, Satinder Singh
2023	A*	`conf`	Human-Timescale Adaptation in an Open-Ended Task Space. ICML Jakob Bauer, Kate Baumli, Feryal M. P. Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, Vibhavari Dasagi, Lucy Gonzalez, Karol Gregor, Edward Hughes, Sheleem Kashem, Maria Loks-Thompson, Hannah Openshaw, Jack Parker-Holder, Shreya Pathak, Nicolas Perez Nieves, Nemanja Rakicevic, Tim Rocktäschel, Yannick Schroecker, Satinder Singh, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, Lei M. Zhang
2023	A*	`conf`	In-context Reinforcement Learning with Algorithm Distillation. ICLR Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, DJ Strouse, Steven Stenberg Hansen, Angelos Filos, Ethan Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih
2023	A*	`conf`	Large Language Models can Implement Policy Iteration. NeurIPS Ethan Brooks, Logan Walls, Richard L. Lewis, Satinder Singh
2023	J	`jnl`	On the Convergence of Bounded Agents. CoRR David Abel, André Barreto, Hado van Hasselt, Benjamin Van Roy, Doina Precup, Satinder Singh
2023	A*	`conf`	Optimistic Meta-Gradients. NeurIPS Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado Philip van Hasselt, András György, Satinder Singh
2023	J	`jnl`	Optimistic Meta-Gradients. CoRR Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado van Hasselt, András György, Satinder Singh
2023	J	`jnl`	POMRL: No-Regret Learning-to-Plan with Increasing Horizons. Trans. Mach. Learn. Res. Khimya Khetarpal, Claire Vernade, Brendan O'Donoghue, Satinder Singh, Tom Zahavy
2023	A*	`conf`	ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs. ICML Ted Moskovitz, Brendan O'Donoghue, Vivek Veeriah, Sebastian Flennerhag, Satinder Singh, Tom Zahavy
2023	J	`jnl`	ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs. CoRR Ted Moskovitz, Brendan O'Donoghue, Vivek Veeriah, Sebastian Flennerhag, Satinder Singh, Tom Zahavy
2023	J	`jnl`	Risk-aware analysis for interpretations of probabilistic achievement and maintenance commitments. Artif. Intell. Qi Zhang, Edmund H. Durfee, Satinder Singh
2023	A*	`conf`	Structured State Space Models for In-Context Reinforcement Learning. NeurIPS Chris Lu, Yannick Schroecker, Albert Gu, Emilio Parisotto, Jakob N. Foerster, Satinder Singh, Feryal M. P. Behbahani
2023	J	`jnl`	Structured State Space Models for In-Context Reinforcement Learning. CoRR Chris Lu, Yannick Schroecker, Albert Gu, Emilio Parisotto, Jakob N. Foerster, Satinder Singh, Feryal M. P. Behbahani
2022	A*	`conf`	Adaptive Pairwise Weights for Temporal Credit Assignment. AAAI Zeyu Zheng, Risto Vuorio, Richard L. Lewis, Satinder Singh
2022	A*	`conf`	Approximate Value Equivalence. NeurIPS Christopher Grimm, André Barreto, Satinder Singh
2022	A*	`conf`	Bootstrapped Meta-Learning. ICLR Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh
2022	J	`jnl`	Discovering Evolution Strategies via Meta-Black-Box Optimization. CoRR Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dallibard, Chris Lu, Satinder Singh, Sebastian Flennerhag
2022	J	`jnl`	Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality. CoRR Tom Zahavy, Yannick Schroecker, Feryal M. P. Behbahani, Kate Baumli, Sebastian Flennerhag, Shaobo Hou, Satinder Singh
2022	J	`jnl`	GrASP: Gradient-Based Affordance Selection for Planning. CoRR Vivek Veeriah, Zeyu Zheng, Richard L. Lewis, Satinder Singh
2022	J	`jnl`	In-Context Policy Iteration. CoRR Ethan Brooks, Logan Walls, Richard L. Lewis, Satinder Singh
2022	J	`jnl`	In-context Reinforcement Learning with Algorithm Distillation. CoRR Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, DJ Strouse, Steven Hansen, Angelos Filos, Ethan Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih
2022	J	`jnl`	Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning. CoRR Julien Pérolat, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas W. Anthony, Stephen McAleer, Romuald Elie, Sarah H. Cen, Zhe Wang, Audrunas Gruslys, Aleksandra Malysheva, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Rémi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls
2022	—	`conf`	Meta-Gradients in Non-Stationary Environments. CoLLAs Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker, David Abel, Tom Zahavy, Satinder Singh
2022	J	`jnl`	Meta-Gradients in Non-Stationary Environments. CoRR Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker, David Abel, Tom Zahavy, Satinder Singh
2022	A*	`conf`	On the Expressivity of Markov Reward (Extended Abstract). IJCAI David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
2022	J	`jnl`	POMRL: No-Regret Learning-to-Plan with Increasing Horizons. CoRR Khimya Khetarpal, Claire Vernade, Brendan O'Donoghue, Satinder Singh, Tom Zahavy
2022	A*	`conf`	Palm up: Playing in the Latent Manifold for Unsupervised Pretraining. NeurIPS Hao Liu, Tom Zahavy, Volodymyr Mnih, Satinder Singh
2022	J	`jnl`	Palm up: Playing in the Latent Manifold for Unsupervised Pretraining. CoRR Hao Liu, Tom Zahavy, Volodymyr Mnih, Satinder Singh
2022	A*	`conf`	Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction. NeurIPS Dilip Arumugam, Satinder Singh
2022	J	`jnl`	Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction. CoRR Dilip Arumugam, Satinder Singh
2021	J	`jnl`	Bootstrapped Meta-Learning. CoRR Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh
2021	J	`jnl`	Discovering Diverse Nearly Optimal Policies withSuccessor Features. CoRR Tom Zahavy, Brendan O'Donoghue, André Barreto, Volodymyr Mnih, Sebastian Flennerhag, Satinder Singh
2021	A*	`conf`	Discovering a set of policies for the worst case reward. ICLR Tom Zahavy, André Barreto, Daniel J. Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh
2021	J	`jnl`	Discovering a set of policies for the worst case reward. CoRR Tom Zahavy, André Barreto, Daniel J. Mankowitz, Shaobo Hou, Brendan O'Donoghue, Iurii Kemaev, Satinder Singh
2021	A*	`conf`	Discovery of Options via Meta-Learned Subgoals. NeurIPS Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh
2021	J	`jnl`	Discovery of Options via Meta-Learned Subgoals. CoRR Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh
2021	A*	`conf`	Efficient Querying for Cooperative Probabilistic Commitments. AAAI Qi Zhang, Edmund H. Durfee, Satinder Singh
2021	A*	`conf`	Learning State Representations from Random Deep Action-conditional Predictions. NeurIPS Zeyu Zheng, Vivek Veeriah, Risto Vuorio, Richard L. Lewis, Satinder Singh
2021	J	`jnl`	Learning State Representations from Random Deep Action-conditional Predictions. CoRR Zeyu Zheng, Vivek Veeriah, Risto Vuorio, Richard L. Lewis, Satinder Singh
2021	J	`jnl`	Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks. CoRR Janarthanan Rajendran, Jonathan K. Kummerfeld, Satinder Singh
2021	A*	`conf`	On the Expressivity of Markov Reward. NeurIPS David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
2021	J	`jnl`	On the Expressivity of Markov Reward. CoRR David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
2021	J	`jnl`	Pairwise Weights for Temporal Credit Assignment. CoRR Zeyu Zheng, Risto Vuorio, Richard L. Lewis, Satinder Singh
2021	A*	`conf`	Proper Value Equivalence. NeurIPS Christopher Grimm, André Barreto, Gregory Farquhar, David Silver, Satinder Singh
2021	J	`jnl`	Proper Value Equivalence. CoRR Christopher Grimm, André Barreto, Gregory Farquhar, David Silver, Satinder Singh
2021	A*	`conf`	Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment. IJCAI Wilka Carvalho, Anthony Liang, Kimin Lee, Sungryull Sohn, Honglak Lee, Richard L. Lewis, Satinder Singh
2021	A*	`conf`	Reinforcement Learning of Implicit and Explicit Control Flow Instructions. ICML Ethan Brooks, Janarthanan Rajendran, Richard L. Lewis, Satinder Singh
2021	J	`jnl`	Reinforcement Learning of Implicit and Explicit Control Flow in Instructions. CoRR Ethan Brooks, Janarthanan Rajendran, Richard L. Lewis, Satinder Singh
2021	A*	`conf`	Reward is enough for convex MDPs. NeurIPS Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins, Satinder Singh
2021	J	`jnl`	Reward is enough for convex MDPs. CoRR Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins, Satinder Singh
2021	J	`jnl`	Reward is enough. Artif. Intell. David Silver, Satinder Singh, Doina Precup, Richard S. Sutton
2020	A*	`conf`	A Self-Tuning Actor-Critic Algorithm. NeurIPS Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
2020	A*	`conf`	Behaviour Suite for Reinforcement Learning. ICLR Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt
2020	A*	`conf`	Discovering Reinforcement Learning Algorithms. NeurIPS Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver
2020	J	`jnl`	Discovering Reinforcement Learning Algorithms. CoRR Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver
2020	J	`jnl`	Efficient Querying for Cooperative Probabilistic Commitments. CoRR Qi Zhang, Edmund H. Durfee, Satinder Singh
2020	A*	`conf`	How Should an Agent Practice? AAAI Janarthanan Rajendran, Richard L. Lewis, Vivek Veeriah, Honglak Lee, Satinder Singh
2020	A*	`conf`	Learning to Play No-Press Diplomacy with Best Response Policy Iteration. NeurIPS Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach
2020	J	`jnl`	Learning to Play No-Press Diplomacy with Best Response Policy Iteration. CoRR Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach
2020	A*	`conf`	Meta-Gradient Reinforcement Learning with an Objective Discovered Online. NeurIPS Zhongwen Xu, Hado Philip van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver
2020	J	`jnl`	Meta-Gradient Reinforcement Learning with an Objective Discovered Online. CoRR Zhongwen Xu, Hado van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver
2020	A*	`conf`	Modeling Probabilistic Commitments for Maintenance Is Inherently Harder than for Achievement. AAAI Qi Zhang, Edmund H. Durfee, Satinder Singh
2020	A*	`conf`	On Efficiency in Hierarchical Reinforcement Learning. NeurIPS Zheng Wen, Doina Precup, Morteza Ibrahimi, André Barreto, Benjamin Van Roy, Satinder Singh
2020	A*	`conf`	Querying to Find a Safe Policy under Uncertain Safety Constraints in Markov Decision Processes. AAAI Shun Zhang, Edmund H. Durfee, Satinder Singh
2020	J	`jnl`	Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in First-person Simulated 3D Environments. CoRR Wilka Carvalho, Anthony Liang, Kimin Lee, Sungryull Sohn, Honglak Lee, Richard L. Lewis, Satinder Singh
2020	A	`conf`	Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles. AISTATS Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder Singh
2020	J	`jnl`	Self-Tuning Deep Reinforcement Learning. CoRR Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
2020	J	`jnl`	Semantics and algorithms for trustworthy commitment achievement under model uncertainty. Auton. Agents Multi Agent Syst. Qi Zhang, Edmund H. Durfee, Satinder Singh
2020	A*	`conf`	The Value Equivalence Principle for Model-Based Reinforcement Learning. NeurIPS Christopher Grimm, André Barreto, Satinder Singh, David Silver
2020	J	`jnl`	The Value Equivalence Principle for Model-Based Reinforcement Learning. CoRR Christopher Grimm, André Barreto, Satinder Singh, David Silver
2020	A*	`conf`	What Can Learned Intrinsic Rewards Capture? ICML Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado van Hasselt, David Silver, Satinder Singh
2019	J	`jnl`	Behaviour Suite for Reinforcement Learning. CoRR Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt
2019	—	`conf`	Computational Strategies for the Trustworthy Pursuit and the Safe Modeling of Probabilistic Maintenance Commitments. AISafety@IJCAI Qi Zhang, Edmund H. Durfee, Satinder Singh
2019	J	`jnl`	Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem. CoRR John Holler, Risto Vuorio, Zhiwei (Tony) Qin, Xiaocheng Tang, Yan Jiao, Tiancheng Jin, Satinder Singh, Chenxi Wang, Jieping Ye
2019	A*	`conf`	Deep Reinforcement Learning for Multi-driver Vehicle Dispatching and Repositioning Problem. ICDM John Holler, Risto Vuorio, Zhiwei (Tony) Qin, Xiaocheng Tang, Yan Jiao, Tiancheng Jin, Satinder Singh, Chenxi Wang, Jieping Ye
2019	A*	`conf`	Discovery of Useful Questions as Auxiliary Tasks. NeurIPS Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
2019	J	`jnl`	Discovery of Useful Questions as Auxiliary Tasks. CoRR Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Richard L. Lewis, Janarthanan Rajendran, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
2019	J	`jnl`	Disentangled Cumulants Help Successor Representations Transfer to New Tasks. CoRR Christopher Grimm, Irina Higgins, André Barreto, Denis Teplyashin, Markus Wulfmeier, Tim Hertweck, Raia Hadsell, Satinder Singh
2019	A*	`conf`	Hindsight Credit Assignment. NeurIPS Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Rémi Munos
2019	J	`jnl`	Hindsight Credit Assignment. CoRR Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hasselt, Greg Wayne, Satinder Singh, Doina Precup, Rémi Munos
2019	J	`jnl`	How Should an Agent Practice? CoRR Janarthanan Rajendran, Richard L. Lewis, Vivek Veeriah, Honglak Lee, Satinder Singh
2019	J	`jnl`	Learning Independently-Obtainable Reward Functions. CoRR Christopher Grimm, Satinder Singh
2019	A*	`conf`	Learning to Communicate and Solve Visual Blocks-World Tasks. AAAI Qi Zhang, Richard L. Lewis, Satinder Singh, Edmund H. Durfee
2019	Misc	`conf`	NE-Table: A Neural key-value table for Named Entities. RANLP Janarthanan Rajendran, Jatin Ganhotra, Xiaoxiao Guo, Mo Yu, Satinder Singh, Lazaros Polymenakos
2019	J	`jnl`	No Press Diplomacy: Modeling Multi-Agent Gameplay. CoRR Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Satinder Singh, Joelle Pineau, Aaron C. Courville
2019	A*	`conf`	No-Press Diplomacy: Modeling Multi-Agent Gameplay. NeurIPS Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Joelle Pineau, Satinder Singh, Aaron C. Courville
2019	J	`jnl`	Object-oriented state editing for HRL. CoRR Victor Bapst, Alvaro Sanchez-Gonzalez, Omar Shams, Kimberly L. Stachenfeld, Peter W. Battaglia, Satinder Singh, Jessica B. Hamrick
2019	—	`ch.`	Online and Scalable Adaptive Cyber Defense. Adversarial and Uncertain Reasoning for Adaptive Cyber Defense Benjamin W. Priest, George Cybenko, Satinder Singh, Massimiliano Albanese, Peng Liu
2019	J	`jnl`	Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles. CoRR Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder Singh
2019	J	`jnl`	What Can Learned Intrinsic Rewards Capture? CoRR Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado van Hasselt, David Silver, Satinder Singh
2018	—	`conf`	Challenges in the Trustworthy Pursuit of Maintenance Commitments Under Uncertainty. TRUST@AAMAS Qi Zhang, Edmund H. Durfee, Satinder Singh
2018	A*	`conf`	Completing State Representations using Spectral Learning. NeurIPS Nan Jiang, Alex Kulesza, Satinder Singh
2018	J	`jnl`	Generative Adversarial Self-Imitation Learning. CoRR Yijie Guo, Junhyuk Oh, Satinder Singh, Honglak Lee
2018	A*	`conf`	Learning End-to-End Goal-Oriented Dialog with Multiple Answers. EMNLP Janarthanan Rajendran, Jatin Ganhotra, Satinder Singh, Lazaros Polymenakos
2018	J	`jnl`	Learning End-to-End Goal-Oriented Dialog with Multiple Answers. CoRR Janarthanan Rajendran, Jatin Ganhotra, Satinder Singh, Lazaros Polymenakos
2018	J	`jnl`	Many-Goals Reinforcement Learning. CoRR Vivek Veeriah, Junhyuk Oh, Satinder Singh
2018	B	`conf`	Markov Decision Processes with Continuous Side Information. ALT Aditya Modi, Nan Jiang, Satinder Singh, Ambuj Tewari
2018	A*	`conf`	Minimax-Regret Querying on Side Effects for Safe Optimality in Factored Markov Decision Processes. IJCAI Shun Zhang, Edmund H. Durfee, Satinder Singh
2018	J	`jnl`	Multistage Attack Graph Security Games: Heuristic Strategies, with Empirical Game-Theoretic Analysis. Secur. Commun. Networks Thanh Hong Nguyen, Mason Wright, Michael P. Wellman, Satinder Singh
2018	J	`jnl`	Named Entities troubling your Neural Methods? Build NE-Table: A neural approach for handling Named Entities. CoRR Janarthanan Rajendran, Jatin Ganhotra, Xiaoxiao Guo, Mo Yu, Satinder Singh
2018	A*	`conf`	On Learning Intrinsic Rewards for Policy Gradient Methods. NeurIPS Zeyu Zheng, Junhyuk Oh, Satinder Singh
2018	J	`jnl`	On Learning Intrinsic Rewards for Policy Gradient Methods. CoRR Zeyu Zheng, Junhyuk Oh, Satinder Singh
2018	A	`conf`	On Querying for Safe Optimality in Factored Markov Decision Processes. AAMAS Shun Zhang, Edmund H. Durfee, Satinder Singh
2018	A*	`conf`	Self-Imitation Learning. ICML Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee
2018	J	`jnl`	Self-Imitation Learning. CoRR Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee
2018	J	`jnl`	The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA. CoRR Jiaxuan Wang, Ian Fox, Jonathan Skaza, Nick Linck, Satinder Singh, Jenna Wiens
2017	—	`conf`	A Stackelberg Game Model for Botnet Data Exfiltration. GameSec Thanh Hong Nguyen, Michael P. Wellman, Satinder Singh
2017	—	`conf`	A Stackelberg Game Model for Botnet Traffic Exfiltration. AAAI Workshops Thanh Hong Nguyen, Michael P. Wellman, Satinder Singh
2017	A*	`conf`	Approximately-Optimal Queries for Planning in Reward-Uncertain Markov Decision Processes. ICAPS Shun Zhang, Edmund H. Durfee, Satinder Singh
2017	—	`conf`	Learning to Query, Reason, and Answer Questions On Ambiguous Texts. ICLR (Poster) Xiaoxiao Guo, Tim Klinger, Clemens Rosenbaum, Joseph P. Bigus, Murray Campbell, Ban Kawas, Kartik Talamadupula, Gerry Tesauro, Satinder Singh
2017	J	`jnl`	Markov Decision Processes with Continuous Side Information. CoRR Aditya Modi, Nan Jiang, Satinder Singh, Ambuj Tewari
2017	A*	`conf`	Minimizing Maximum Regret in Commitment Constrained Sequential Decision Making. ICAPS Qi Zhang, Satinder Singh, Edmund H. Durfee
2017	J	`jnl`	Minimizing Maximum Regret in Commitment Constrained Sequential Decision Making. CoRR Qi Zhang, Satinder Singh, Edmund H. Durfee
2017	—	`conf`	Multi-Stage Attack Graph Security Games: Heuristic Strategies, with Empirical Game-Theoretic Analysis. MTD@CCS Thanh Hong Nguyen, Mason Wright, Michael P. Wellman, Satinder Singh
2017	—	`conf`	Predicting Counselor Behaviors in Motivational Interviewing Encounters. EACL (1) Verónica Pérez-Rosas, Rada Mihalcea, Kenneth Resnicow, Satinder Singh, Lawrence C. An, Kathy J. Goggin, Delwyn Catley
2017	A*	`ed.`	Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA. AAAI Satinder Singh, Shaul Markovitch
2017	—	`conf`	Repeated Inverse Reinforcement Learning. NIPS Kareem Amin, Nan Jiang, Satinder Singh
2017	J	`jnl`	Repeated Inverse Reinforcement Learning. CoRR Kareem Amin, Nan Jiang, Satinder Singh
2017	—	`conf`	Understanding and Predicting Empathic Behavior in Counseling Therapy. ACL (1) Verónica Pérez-Rosas, Rada Mihalcea, Kenneth Resnicow, Satinder Singh, Lawrence C. An
2017	—	`conf`	Value Prediction Network. NIPS Junhyuk Oh, Satinder Singh, Honglak Lee
2017	J	`jnl`	Value Prediction Network. CoRR Junhyuk Oh, Satinder Singh, Honglak Lee
2017	A*	`conf`	Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning. ICML Junhyuk Oh, Satinder Singh, Honglak Lee, Pushmeet Kohli
2017	J	`jnl`	Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning. CoRR Junhyuk Oh, Satinder Singh, Honglak Lee, Pushmeet Kohli
2016	—	`conf`	Building a Motivational Interviewing Dataset. CLPsych@HLT-NAACL Verónica Pérez-Rosas, Rada Mihalcea, Kenneth Resnicow, Satinder Singh, Lawrence C. An
2016	A*	`conf`	Commitment Semantics for Sequential Decision Making under Reward Uncertainty. IJCAI Qi Zhang, Edmund H. Durfee, Satinder Singh, Anna Chen, Stefan J. Witwicki
2016	A*	`conf`	Control of Memory, Active Perception, and Action in Minecraft. ICML Junhyuk Oh, Valliappa Chockalingam, Satinder Singh, Honglak Lee
2016	J	`jnl`	Control of Memory, Active Perception, and Action in Minecraft. CoRR Junhyuk Oh, Valliappa Chockalingam, Satinder Singh, Honglak Lee
2016	A*	`conf`	Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games. IJCAI Xiaoxiao Guo, Satinder Singh, Richard L. Lewis, Honglak Lee
2016	J	`jnl`	Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games. CoRR Xiaoxiao Guo, Satinder Singh, Richard L. Lewis, Honglak Lee
2016	A	`conf`	Gradient Methods for Stackelberg Games. UAI Kareem Amin, Michael P. Wellman, Satinder Singh
2016	A*	`conf`	Improving Predictive State Representations via Gradient Descent. AAAI Nan Jiang, Alex Kulesza, Satinder Singh
2016	J	`jnl`	Multi-task seizure detection: addressing intra-patient variation in seizure morphologies. Mach. Learn. Alexander Van Esbroeck, Landon Smith, Zeeshan Syed, Satinder Singh, Zahi N. Karam
2016	A*	`conf`	On Structural Properties of MDPs that Bound Loss Due to Shallow Planning. IJCAI Nan Jiang, Satinder Singh, Ambuj Tewari
2016	—	`conf`	On the Trustworthy Fulfillment of Commitments. TRUST@AAMAS Edmund H. Durfee, Satinder Singh
2016	—	`conf`	On the Trustworthy Fulfillment of Commitments. AAMAS Workshops (Selected Papers) Edmund H. Durfee, Satinder Singh
2016	A*	`conf`	The Dependence of Effective Planning Horizon on Model Accuracy. IJCAI Nan Jiang, Alex Kulesza, Satinder Singh, Richard L. Lewis
2016	J	`jnl`	Towards Resolving Unidentifiability in Inverse Reinforcement Learning. CoRR Kareem Amin, Satinder Singh
2015	A*	`conf`	Abstraction Selection in Model-based Reinforcement Learning. ICML Nan Jiang, Alex Kulesza, Satinder Singh
2015	—	`conf`	Action-Conditional Video Prediction using Deep Networks in Atari Games. NIPS Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, Satinder Singh
2015	J	`jnl`	Action-Conditional Video Prediction using Deep Networks in Atari Games. CoRR Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, Satinder Singh
2015	—	`conf`	Commitment Semantics for Sequential Decision Making Under Reward Uncertainty. AAAI Fall Symposia Edmund H. Durfee, Satinder Singh
2015	A	`conf`	Low-Rank Spectral Learning with Weighted Loss Functions. AISTATS Alex Kulesza, Nan Jiang, Satinder Singh
2015	A*	`conf`	Spectral Learning of Predictive State Representations with Insufficient Statistics. AAAI Alex Kulesza, Nan Jiang, Satinder Singh
2015	A	`conf`	The Dependence of Effective Planning Horizon on Model Accuracy. AAMAS Nan Jiang, Alex Kulesza, Satinder Singh, Richard L. Lewis
2014	A	`conf`	Characterizing EVOI-Sufficient k-Response Query Sets in Decision Problems. AISTATS Robert Cohn, Satinder Singh, Edmund H. Durfee
2014	J	`jnl`	Computational Rationality: Linking Mechanism and Behavior Through Bounded Utility Maximization. Top. Cogn. Sci. Richard L. Lewis, Andrew Howes, Satinder Singh
2014	—	`conf`	Computationally Rational Saccadic Control: An Explanation of Spillover Effects Based on Sampling from Noisy Perception and Memory. CMCL@ACL Michael Shvartsman, Richard L. Lewis, Satinder Singh
2014	A*	`conf`	Computing Solutions in Infinite-Horizon Discounted Adversarial Patrolling Games. ICAPS Yevgeniy Vorobeychik, Bo An, Milind Tambe, Satinder Singh
2014	—	`conf`	Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning. NIPS Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard L. Lewis, Xiaoshi Wang
2014	Misc	`conf`	Ecologically valid long-term mood monitoring of individuals with bipolar disorder using speech. ICASSP Zahi N. Karam, Emily Mower Provost, Satinder Singh, Jennifer Montgomery, Christopher Archer, Gloria Harrington, Melvin G. McInnis
2014	A*	`conf`	Evaluating Trauma Patients: Addressing Missing Covariates with Joint Optimization. AAAI Alexander Van Esbroeck, Satinder Singh, Ilan Rubinfeld, Zeeshan Syed
2014	A	`conf`	Improving UCT planning via approximate homomorphisms. AAMAS Nan Jiang, Satinder Singh, Richard L. Lewis
2014	J	`jnl`	Learning to Make Predictions In Partially Observable Environments Without a Generative Model. CoRR Erik Talvitie, Satinder Singh
2014	A	`conf`	Low-Rank Spectral Learning. AISTATS Alex Kulesza, N. Raj Rao, Satinder Singh
2014	J	`jnl`	Optimal Rewards for Cooperative Agents. IEEE Trans. Auton. Ment. Dev. Bingyao Liu, Satinder Singh, Richard L. Lewis, Shiyin Qin
2014	A*	`conf`	Predicting Postoperative Atrial Fibrillation from Independent ECG Components. AAAI Chih-Chun Chia, James Blum, Zahi N. Karam, Satinder Singh, Zeeshan Syed
2014	J	`jnl`	Utility Maximization and Bounds on Human Information Processing. Top. Cogn. Sci. Andrew Howes, Richard L. Lewis, Satinder Singh
2013	J	`jnl`	Approximate Planning for Factored POMDPs using Belief State Simplification CoRR David A. McAllester, Satinder Singh
2013	J	`jnl`	Fast Planning in Stochastic Games CoRR Michael J. Kearns, Yishay Mansour, Satinder Singh
2013	J	`jnl`	Graphical Models for Game Theory CoRR Michael J. Kearns, Michael L. Littman, Satinder Singh
2013	—	`conf`	Linking Context to Evaluation in the Design of Safety Critical Interfaces. HCI (1) Michael Feary, Dorrit Billman, Xiuli Chen, Andrew Howes, Richard L. Lewis, Lance Sherry, Satinder Singh
2013	J	`jnl`	Nash Convergence of Gradient Dynamics in Iterated General-Sum Games CoRR Satinder Singh, Michael J. Kearns, Yishay Mansour
2013	J	`jnl`	On the Complexity of Policy Iteration CoRR Yishay Mansour, Satinder Singh
2013	—	`conf`	Reward Mapping for Transfer in Long-Lived Agents. NIPS Xiaoxiao Guo, Satinder Singh, Richard L. Lewis
2013	J	`jnl`	The Adaptive Nature of Eye Movements in Linguistic Tasks: How Payoff and Architecture Shape Speed-Accuracy Trade-Offs. Top. Cogn. Sci. Richard L. Lewis, Michael Shvartsman, Satinder Singh
2012	A*	`conf`	Computing Stackelberg Equilibria in Discounted Stochastic Games. AAAI Yevgeniy Vorobeychik, Satinder Singh
2012	J	`jnl`	Knowledge Combination in Graphical Multiagent Model CoRR Quang Duong, Michael P. Wellman, Satinder Singh
2012	A	`conf`	Learning and predicting dynamic networked behavior with graphical multiagent models. AAMAS Quang Duong, Michael P. Wellman, Satinder Singh, Michael J. Kearns
2012	A*	`conf`	Lossy stochastic game abstraction with bounds. EC Tuomas Sandholm, Satinder Singh
2012	J	`jnl`	Optimal Coordinated Planning Amongst Self-Interested Agents with Private State. CoRR Ruggiero Cavallo, David C. Parkes, Satinder Singh
2012	—	`conf`	Optimal rewards in multiagent teams. ICDL-EPIROB Bingyao Liu, Satinder Singh, Richard L. Lewis, Shiyin Qin
2012	A	`conf`	Planning and evaluating multiagent influences under reward uncertainty. AAMAS Stefan J. Witwicki, Inn-Tung Chen, Edmund H. Durfee, Satinder Singh
2012	J	`jnl`	Predictive Linear-Gaussian Models of Stochastic Dynamical Systems CoRR Matthew R. Rudary, Satinder Singh, David Wingate
2012	J	`jnl`	Predictive State Representations: A New Theory for Modeling Dynamical Systems CoRR Satinder Singh, Michael R. James, Matthew R. Rudary
2012	J	`jnl`	Reports of the AAAI 2011 Conference Workshops. AI Mag. Noa Agmon, Vikas Agrawal, David W. Aha, Yiannis Aloimonos, Donagh Buckley, Prashant Doshi, Christopher W. Geib, Floriana Grasso, Nancy L. Green, Benjamin Johnston, Burt Kaliski, Christopher Kiekintveld, Edith Law, Henry Lieberman, Ole J. Mengshoel, Ted Metzler, Joseph Modayil, Douglas W. Oard, Nilufer Onder, Barry O'Sullivan, Katerina Pastra, Doina Precup, Sowmya Ramachandran, Chris Reed, Sanem Sariel Talay, Ted Selker, Lokendra Shastri, Stephen F. Smith, Satinder Singh, Siddharth Srivastava, Gita Sukthankar, David C. Uthus, Mary-Anne Williams
2012	A*	`conf`	Security Games with Limited Surveillance. AAAI Bo An, David Kempe, Christopher Kiekintveld, Eric Shieh, Satinder Singh, Milind Tambe, Yevgeniy Vorobeychik
2012	—	`conf`	Security Games with Limited Surveillance: An Initial Report. AAAI Spring Symposium: Game Theory for Security, Sustainability, and Health Bo An, David Kempe, Christopher Kiekintveld, Eric Anyung Shieh, Satinder Singh, Milind Tambe, Yevgeniy Vorobeychik
2012	A	`conf`	Strong mitigation: nesting search for good policies within search for good reward. AAMAS Jeshua Bratman, Satinder Singh, Jonathan Sorg, Richard L. Lewis
2012	J	`jnl`	Variance-Based Rewards for Approximate Bayesian Reinforcement Learning CoRR Jonathan Sorg, Satinder Singh, Richard L. Lewis
2011	J	`jnl`	ATTac-2000: An Adaptive Autonomous Bidding Agent CoRR Michael J. Kearns, Michael L. Littman, Satinder Singh, Peter Stone
2011	A*	`conf`	Comparing Action-Query Strategies in Semi-Autonomous Agents. AAAI Robert Cohn, Edmund H. Durfee, Satinder Singh
2011	A	`conf`	Comparing action-query strategies in semi-autonomous agents. AAMAS Robert Cohn, Edmund H. Durfee, Satinder Singh
2011	J	`jnl`	Learning to Make Predictions In Partially Observable Environments Without a Generative Model. J. Artif. Intell. Res. Erik Talvitie, Satinder Singh
2011	—	`conf`	Modeling Information Diffusion in Networks with Unobserved Links. SocialCom/PASSAT Quang Duong, Michael P. Wellman, Satinder Singh
2011	A*	`conf`	Optimal Rewards versus Leaf-Evaluation Heuristics in Planning Agents. AAAI Jonathan Sorg, Satinder Singh, Richard L. Lewis
2011	J	`jnl`	Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System CoRR Michael J. Kearns, Diane J. Litman, Satinder Singh, Marilyn A. Walker
2010	J	`jnl`	Dynamic Incentive Mechanisms. AI Mag. David C. Parkes, Ruggiero Cavallo, Florin Constantin, Satinder Singh
2010	A	`conf`	History-dependent graphical multiagent models. AAMAS Quang Duong, Michael P. Wellman, Satinder Singh, Yevgeniy Vorobeychik
2010	A*	`conf`	Internal Rewards Mitigate Agent Boundedness. ICML Jonathan Sorg, Satinder Singh, Richard L. Lewis
2010	J	`jnl`	Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective. IEEE Trans. Auton. Ment. Dev. Satinder Singh, Richard L. Lewis, Andrew G. Barto, Jonathan Sorg
2010	A	`conf`	Linear options. AAMAS Jonathan Sorg, Satinder Singh
2010	—	`conf`	Reward Design via Online Gradient Ascent. NIPS Jonathan Sorg, Satinder Singh, Richard L. Lewis
2010	—	`conf`	Selecting Operator Queries Using Expected Myopic Gain. IAT Robert Cohn, Michael Maxim, Edmund H. Durfee, Satinder Singh
2010	A	`conf`	Variance-Based Rewards for Approximate Bayesian Reinforcement Learning. UAI Jonathan Sorg, Satinder Singh, Richard L. Lewis
2009	A*	`conf`	Learning Graphical Game Models. IJCAI Quang Duong, Yevgeniy Vorobeychik, Satinder Singh, Michael P. Wellman
2009	A*	`conf`	Maintaining Predictions over Time without a Model. IJCAI Erik Talvitie, Satinder Singh
2009	—	`conf`	SarsaLandmark: an algorithm for learning in POMDPs with landmarks. AAMAS (1) Michael R. James, Satinder Singh
2009	—	`conf`	Transfer via soft homomorphisms. AAMAS (2) Jonathan Sorg, Satinder Singh
2008	—	`conf`	Approximate predictive state representations. AAMAS (1) Britton Wolfe, Michael R. James, Satinder Singh
2008	Misc	`conf`	Building Incomplete but Accurate Models. ISAIM Erik Talvitie, Britton Wolfe, Satinder Singh
2008	A*	`conf`	Efficiently learning linear-linear exponential family predictive representations of state. ICML David Wingate, Satinder Singh
2008	A	`conf`	Knowledge Combination in Graphical Multiagent Models. UAI Quang Duong, Michael P. Wellman, Satinder Singh
2008	Misc	`conf`	Predictive Linear-Gaussian Models of Dynamical Systems with Vector-Valued Actions and Observations. ISAIM Matthew R. Rudary, Satinder Singh
2008	—	`conf`	Simple Local Models for Complex Dynamical Systems. NIPS Erik Talvitie, Satinder Singh
2007	A*	`conf`	Abstraction in Predictive State Representations. AAAI Vishal Soni, Satinder Singh
2007	A*	`conf`	An Experts Algorithm for Transfer Learning. IJCAI Erik Talvitie, Satinder Singh
2007	A	`conf`	Constraint satisfaction algorithms for graphical games. AAMAS Vishal Soni, Satinder Singh, Michael P. Wellman
2007	—	`conf`	DaNaLIX: a domain-adaptive natural language interface for querying XML. SIGMOD Conference Yunyao Li, Ishan Chaudhuri, Huahai Yang, Satinder Singh, H. V. Jagadish
2007	A*	`conf`	Enabling Domain-Awareness for a Generic Natural Language Interface. AAAI Yunyao Li, Ishan Chaudhuri, Huahai Yang, Satinder Singh, H. V. Jagadish
2007	—	`conf`	Exponential Family Predictive Representations of State. NIPS David Wingate, Satinder Singh
2007	J	`jnl`	Learning payoff functions in infinite games. Mach. Learn. Yevgeniy Vorobeychik, Michael P. Wellman, Satinder Singh
2007	A	`conf`	On discovery and learning of models with predictive representations of state for agents with continuous actions and observations. AAMAS David Wingate, Satinder Singh
2007	A*	`conf`	Relational Knowledge with Predictive State Representations. IJCAI David Wingate, Vishal Soni, Britton Wolfe, Satinder Singh
2006	J	`jnl`	Cobot in LambdaMOO: An Adaptive Social Statistics Agent. Auton. Agents Multi Agent Syst. Charles Lee Isbell Jr., Michael J. Kearns, Satinder Singh, Christian R. Shelton, Peter Stone, David P. Kormann
2006	A*	`conf`	Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems. ICML David Wingate, Satinder Singh
2006	A*	`conf`	Mixtures of Predictive Linear Gaussian Models for Nonlinear, Stochastic Dynamical Systems. AAAI David Wingate, Satinder Singh
2006	A	`conf`	Optimal Coordinated Planning Amongst Self-Interested Agents with Private State. UAI Ruggiero Cavallo, David C. Parkes, Satinder Singh
2006	A*	`conf`	Predictive linear-Gaussian models of controlled stochastic dynamical systems. ICML Matthew R. Rudary, Satinder Singh
2006	A*	`conf`	Predictive state representations with options. ICML Britton Wolfe, Satinder Singh
2006	A*	`conf`	Using Homomorphisms to Transfer Options across Continuous Reinforcement Learning Domains. AAAI Vishal Soni, Satinder Singh
2005	A*	`conf`	Combining Memory and Landmarks with Predictive State Representations. IJCAI Michael R. James, Britton Wolfe, Satinder Singh
2005	A*	`conf`	Learning Payoff Functions in Infinite Games. IJCAI Yevgeniy Vorobeychik, Michael P. Wellman, Satinder Singh
2005	A*	`conf`	Learning predictive state representations in dynamical systems without reset. ICML Britton Wolfe, Michael R. James, Satinder Singh
2005	—	`conf`	Off-policy Learning with Options and Recognizers. NIPS Doina Precup, Richard S. Sutton, Cosmin Paduraru, Anna Koop, Satinder Singh
2005	A*	`conf`	Planning in Models that Combine Memory with Predictive Representations of State. AAAI Michael R. James, Satinder Singh
2005	A	`conf`	Predictive Linear-Gaussian Models of Stochastic Dynamical Systems. UAI Matthew R. Rudary, Satinder Singh, David Wingate
2005	J	`jnl`	Reports on the 2004 AAAI Fall Symposia. AI Mag. Nicholas L. Cassimatis, Sean Luke, Simon D. Levy, Ross W. Gayler, Pentti Kanerva, Chris Eliasmith, Timothy W. Bickmore, Alan C. Schultz, Randall Davis, James A. Landay, Robert C. Miller, Eric Saund, Thomas F. Stahovich, Michael L. Littman, Satinder Singh, Shlomo Argamon, Shlomo Dubnov
2005	J	`jnl`	Strategic Interactions in a Supply Chain Game. Comput. Intell. Michael P. Wellman, Joshua Estelle, Satinder Singh, Yevgeniy Vorobeychik, Christopher Kiekintveld, Vishal Soni
2004	A*	`conf`	Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning. ICML Matthew R. Rudary, Satinder Singh, Martha E. Pollack
2004	—	`conf`	Approximately Efficient Online Mechanism Design. NIPS David C. Parkes, Satinder Singh, Dimah Yanovsky
2004	A*	`conf`	Computing approximate bayes-nash equilibria in tree-games of incomplete information. EC Satinder Singh, Vishal Soni, Michael P. Wellman
2004	A*	`conf`	Distributed Feedback Control for Decision Making on Supply Chains. ICAPS Christopher Kiekintveld, Michael P. Wellman, Satinder Singh, Joshua Estelle, Yevgeniy Vorobeychik, Vishal Soni, Matthew R. Rudary
2004	—	`conf`	Intrinsically Motivated Reinforcement Learning. NIPS Satinder Singh, Andrew G. Barto, Nuttapong Chentanez
2004	—	`conf`	Learning Payoff Functions in Infinite Games. AAAI Technical Report (2) Yevgeniy Vorobeychik, Michael P. Wellman, Satinder Singh
2004	A*	`conf`	Learning and discovery of predictive state representations in dynamical systems with reset. ICML Michael R. James, Satinder Singh
2004	C	`conf`	Planning with predictive state representations. ICMLA Michael R. James, Satinder Singh, Michael L. Littman
2004	A	`conf`	Predictive State Representations: A New Theory for Modeling Dynamical Systems. UAI Satinder Singh, Michael R. James, Matthew R. Rudary
2004	—	`conf`	Strategic Interactions in the TAC 2003 Supply Chain Tournament. Computers and Games Joshua Estelle, Yevgeniy Vorobeychik, Michael P. Wellman, Satinder Singh, Christopher Kiekintveld, Vishal Soni
2004	J	`jnl`	Value-driven procurement in the TAC supply chain game. SIGecom Exch. Christopher Kiekintveld, Michael P. Wellman, Satinder Singh, Vishal Soni
2003	—	`conf`	A Nonlinear Predictive State Representation. NIPS Matthew R. Rudary, Satinder Singh
2003	—	`conf`	An MDP-Based Approach to Online Mechanism Design. NIPS David C. Parkes, Satinder Singh
2003	A*	`conf`	Learning Predictive State Representations. ICML Satinder Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, Peter Stone
2002	—	`conf`	CobotDS: A Spoken Dialogue System for Chat. AAAI/IAAI Michael J. Kearns, Charles Lee Isbell Jr., Satinder Singh, Diane J. Litman, Jessica Howe
2002	J	`jnl`	Introduction. Mach. Learn. Satinder Singh
2002	J	`jnl`	Near-Optimal Reinforcement Learning in Polynomial Time. Mach. Learn. Michael J. Kearns, Satinder Singh
2002	J	`jnl`	Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System. J. Artif. Intell. Res. Satinder Singh, Diane J. Litman, Michael J. Kearns, Marilyn A. Walker
2001	—	`conf`	A social reinforcement learning agent. Agents Charles Lee Isbell Jr., Christian R. Shelton, Michael J. Kearns, Satinder Singh, Peter Stone
2001	J	`jnl`	ATTac-2000: An Adaptive Autonomous Bidding Agent. J. Artif. Intell. Res. Peter Stone, Michael L. Littman, Satinder Singh, Michael J. Kearns
2001	—	`conf`	ATTac-2000: an adaptive autonomous bidding agent. Agents Peter Stone, Michael L. Littman, Satinder Singh, Michael J. Kearns
2001	—	`conf`	An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games. NIPS Michael L. Littman, Michael J. Kearns, Satinder Singh
2001	—	`conf`	Cobot: A Social Reinforcement Learning Agent. NIPS Charles Lee Isbell Jr., Christian R. Shelton, Michael J. Kearns, Satinder Singh, Peter Stone
2001	—	`conf`	FAucS : An FCC Spectrum Auction Simulator for Autonomous Bidding Agents. WELCOM János A. Csirik, Michael L. Littman, Satinder Singh, Peter Stone
2001	A	`conf`	Graphical Models for Game Theory. UAI Michael J. Kearns, Michael L. Littman, Satinder Singh
2001	—	`conf`	Predictive Representations of State. NIPS Michael L. Littman, Richard S. Sutton, Satinder Singh
2000	A*	`conf`	A Boosting Approach to Topic Spotting on Subdialogues. ICML Kary L. Myers, Michael J. Kearns, Satinder Singh, Marilyn A. Walker
2000	B	`conf`	Automatic Optimization of Dialogue Management. COLING Diane J. Litman, Michael S. Kearns, Satinder Singh, Marilyn A. Walker
2000	A*	`conf`	Bias-Variance Error Bounds for Temporal Difference Updates. COLT Michael J. Kearns, Satinder Singh
2000	—	`conf`	Cobot in LambdaMOO: A Social Statistics Agent. AAAI/IAAI Charles Lee Isbell Jr., Michael J. Kearns, David P. Kormann, Satinder Singh, Peter Stone
2000	J	`jnl`	Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms. Mach. Learn. Satinder Singh, Tommi S. Jaakkola, Michael L. Littman, Csaba Szepesvári
2000	A*	`conf`	Eligibility Traces for Off-Policy Policy Evaluation. ICML Doina Precup, Richard S. Sutton, Satinder Singh
2000	—	`conf`	Empirical Evaluation of a Reinforcement Learning Spoken Dialogue System. AAAI/IAAI Satinder Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker
2000	A	`conf`	Fast Planning in Stochastic Games. UAI Michael J. Kearns, Yishay Mansour, Satinder Singh
2000	A	`conf`	Nash Convergence of Gradient Dynamics in General-Sum Games. UAI Satinder Singh, Michael J. Kearns, Yishay Mansour
2000	C	`conf`	Reinforcement Learning for 3 vs. 2 Keepaway RoboCup Peter Stone, Richard S. Sutton, Satinder Singh
1999	A	`conf`	Approximate Planning for Factored POMDPs using Belief State Simplification. UAI David A. McAllester, Satinder Singh
1999	J	`jnl`	Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning. Artif. Intell. Richard S. Sutton, Doina Precup, Satinder Singh
1999	A	`conf`	On the Complexity of Policy Iteration. UAI Yishay Mansour, Satinder Singh
1999	—	`conf`	Policy Gradient Methods for Reinforcement Learning with Function Approximation. NIPS Richard S. Sutton, David A. McAllester, Satinder Singh, Yishay Mansour
1999	—	`conf`	Reinforcement Learning for Spoken Dialogue Systems. NIPS Satinder Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker
1998	J	`jnl`	Analytical Mean Squared Error Curves for Temporal Difference Learning. Mach. Learn. Satinder Singh, Peter Dayan
1998	—	`conf`	Experimental Results on Learning Stochastic Memoryless Policies for Partially Observable Markov Decision Processes. NIPS John K. Williams, Satinder Singh
1998	—	`conf`	Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms. NIPS Michael J. Kearns, Satinder Singh
1998	—	`conf`	Improved Switching among Temporally Abstract Actions. NIPS Richard S. Sutton, Satinder Singh, Doina Precup, Balaraman Ravindran
1998	A*	`conf`	Intra-Option Learning about Temporally Abstract Actions. ICML Richard S. Sutton, Doina Precup, Satinder Singh
1998	A*	`conf`	Near-Optimal Reinforcement Learning in Polynominal Time. ICML Michael J. Kearns, Satinder Singh
1998	—	`conf`	Optimizing Admission Control while Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning. NIPS Timothy X. Brown, Hui Tong, Satinder Singh
1998	—	`conf`	Theoretical Results on Reinforcement Learning with Temporally Abstract Options. ECML Doina Precup, Richard S. Sutton, Satinder Singh
1998	A*	`conf`	Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes. ICML John Loch, Satinder Singh
1997	—	`conf`	How to Dynamically Merge Markov Decision Processes. NIPS Satinder Singh, David Cohn
1996	—	`conf`	Analytical Mean Squared Error Curves in Temporal Difference Learning. NIPS Satinder Singh, Peter Dayan
1996	—	`conf`	Predicting Lifetimes in Dynamically Allocated Memory. NIPS David A. Cohn, Satinder Singh
1996	—	`conf`	Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems. NIPS Satinder Singh, Dimitri P. Bertsekas
1995	—	`conf`	Improving Policies without Measuring Merits. NIPS Peter Dayan, Satinder Singh
1994	—	`conf`	Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems. NIPS Tommi S. Jaakkola, Satinder Singh, Michael I. Jordan
1994	—	`conf`	Reinforcement Learning with Soft State Aggregation. NIPS Satinder Singh, Tommi S. Jaakkola, Michael I. Jordan
1993	—	`conf`	Convergence of Stochastic Iterative Dynamic Programming Algorithms. NIPS Tommi S. Jaakkola, Michael I. Jordan, Satinder Singh
1993	—	`conf`	Robust Reinforcement Learning in Motion Planning. NIPS Satinder Singh, Andrew G. Barto, Roderic A. Grupen, Christopher I. Connolly
1991	—	`conf`	The Efficient Learning of Multiple Task Sequences. NIPS Satinder Singh