2017+ Papers

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  • How Should An Agent Practice?
    by Janarthanan Rajendran, Richard Lewis, Vivek Veeriah, Honglak Lee, and Satinder Singh.
    In Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.
    pdf.

  • Modeling Probabilistic Commitments for Maintainance is Inherently Harder than for Achievement
    by Qi Zhang, Edmund Durfee, and Satinder Singh.
    In Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.
    pdf.

  • Querying to Find a Safe Policy under Uncertain Safety Constraints in Markov Decision Processes
    by Shun Zhang, Edmund Durfee, and Satinder Singh.
    In Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.
    pdf.

  • Discovery of Useful Questions as Auxiliary Tasks
    by Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Richard Lewis, Janarthanan Rajendran, Junhyuk Oh, Hado van Hsselt, David Silver, and Satinder Singh.
    In Neural Information Processing Systems (NeurIPS), 2019.
    arxiv version.

  • Hindsight Credit Assignment
    by Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheslaghi Azar, Bilal Piot, Nicolas Heess, Hado van Hsselt, Gregory Wayne, Satinder Singh, Doina Precup, and Remi Munos.
    In Neural Information Processing Systems (NeurIPS), 2019.
    pdf.

  • No Press Diplomacy: Modeling Multi-Agent Gameplay
    by Philip Paquette, Yuchen Lu, Steven Bocco, Max ). Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Satinder Singh, Joelle Pineau, and Aaron Courville.
    In Neural Information Processing Systems (NeurIPS), 2019.
    arxiv version.

  • Deep Reinforcment Learning for Dynamic Multi-Driver Dispatching and Repositioning Problem
    by John Holler, Risto Vuorio, Tiancheng Jin, Satinder Singh, Zhiwei Qin, Jieping Ye, Xiaocheng Tan, Yan Jiao, and Chenxi Wang.
    In International Conference on Data Mining (ICDM-Short Paper), 2019.
    pdf.

  • Sample Complexity of Reinforcement Learning Using Linearly Combined Model Ensembles
    by Aditya Modi, Nan Jiang, Ambuj Tewari, and Satinder Singh.
    arXiv version.

  • Learning Independently-Obtainable Reward Functions
    by Christopher Grimm and Satinder Singh.
    arXiv version.

  • Many-Goals Reinforcement Learning
    by Vivek Veeriah, Junhyuk Oh, and Satinder Singh.
    arXiv version.

  • Learning to Communicate and Solve Visual Blocks-World Tasks
    by Qi Zhang, Richard Lewis, Satinder Singh, and Edmund Durfee.
    In Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019.
    pdf.

  • On Learning Intrinsic Rewards for Policy Gradient Methods
    by Zeyu Zheng, Junhyuk Oh, and Satinder Singh.
    In Neural Information Processing Systems (NIPS), 2018.
    arXiv version.

  • Completing State Representations Using Spectral Learning
    by Nan Jiang, Alex Kulesza, and Satinder Singh.
    In Neural Information Processing Systems (NIPS), 2018.
    pdf.

  • Learning End-to-End Goal-Oriented Dialog with Multiple Answers
    by Janarthanan Rajendran, Jatin Ganhotra, Satinder Singh, and Lazaros Polymenakos.
    In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.
    pdf.

  • Self-Imitation Learning
    by Junhyuk Oh, Yijie Guo, Satinder Singh, and Honglak Lee.
    In International Conference on Machine Learning (ICML), 2018.
    arXiv version.

  • Minimax-Regret Querying on Side Effects for Safe Optimality in Factored Markov Decision Processes
    by Shun Zhang, Edmund Durfee, and Satinder Singh.
    In International Joint Conference on Artificial Intelligence (IJCAI), 2018.
    pdf.

  • Markov Decision Processes with Continuous Side Information
    by Aditya Modi, Nan Jiang, Satinder Singh, and Ambuj Tewari.
    In International Conference on Algorithmic Learning Theory (ALT), 2018.
    conf pdf, arXiv link.

  • The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA
    by Jiaxuan Wang, Ian Fox, Jonathan Skaza, Nick Linck, Satinder Singh, and Jenna Wiens.
    In Sloan Sports Analytics Conference, 2018.
    arXiv link.

  • Value Prediction Networks
    by Junhyuk Oh, Satinder Singh, Honglak Lee.
    In Neural Information Processing Systems (NIPS), 2017.
    arXiv link.

  • Repeated Inverse Reinforcement Learning
    by Kareem Amin, Nan Jiang, and Satinder Singh.
    In Neural Information Processing Systems (NIPS), 2017.
    arXiv link.

  • A Big Step for AI
    by Satinder Singh.
    In Nature: News & Views, 2017.
    pdf.

  • Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning
    by Junhyuk Oh, Satinder Singh, Honglak Lee, and Pushmeet Kohli.
    In International Conference on Machine Learning (ICML), 2017.
    pdf.

  • A Stackelberg Game Model for Botnet Data Exfiltration
    by Thang Nguyen, Michael Wellman, and Satinder Singh.
    In Proceedings of the 8th Conference on Decision and Game Theory for Security (GameSec), 2017.
    pdf.

  • Learning to Query, Reason, and Answer Questions on Ambiguous Texts.
    by Xiaoxiao Guo, Tim Klinger, Clemens Rosenbaum, Jospeh Bigus, Murray Campbell, Ban Kawas, Kartik Talamadupula, Gerald Tesauro, and Satinder Singh.
    In 5th International Conference on Learning Representations (ICLR), 2017.
    pdf.

  • Approximately-Optimal Queries for Planning in Reward-Uncertain Markov Decision Processes.
    by Shun Zhang, Edmund Durfee, and Satinder Singh.
    In 27th International Conference on Automated Planning and Scheduling (ICAPS), 2017.
    pdf.

  • Minimizing Maximum Regret in Commitment Constrained Sequential Decision Making.
    by Qi Zhang, Satinder Singh, and Edmund Durfee.
    In 27th International Conference on Automated Planning and Scheduling (ICAPS), 2017.
    pdf.

  • Predicting Counselor Behaviors in Motivational Interviewing Encounters.
    by Veronica Perez-Rosas, Rada Mihalcea, Kenneth Resnicow, Satinder Singh, Lawrence An, Kathy J. Goggin, and Delwyn Catley.
    In Proceedings of the European Association of Computational Linguistics, (EACL) 2017.
    pdf.

    All My Papers in Reverse Chronological Order