2018


  1. Bei Peng, James MacGlashan, Robert Loftin, Michael L. Littman, David L. Roberts, and Matthew E. Taylor. Curriculum Design for Machine Learners in Sequential Decision Tasks. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018.
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2017


  1. James MacGlashan, Mark K. Ho, Robert Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, and Michael L. Littman. Interactive Learning from Policy-Dependent Human Feedback. In Proceedings of the 34th International Conferences on Machine Learning (ICML), August 2017. 25% acceptance rate.
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  2. Bei Peng, James MacGlashan, Robert Loftin, Michael L. Littman, David L. Roberts, and Matthew E. Taylor. Curriculum Design for Machine Learners in Sequential Decision Tasks (Extended Abstract). In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2017. Extended abstract: 26% acceptance rate for papers, additional 22% for extended abstracts.
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  3. Bei Peng, James MacGlashan, Robert Loftin, Michael L. Littman, David L. Roberts, and Matthew E. Taylor. Curriculum Design for Machine Learners in Sequential Decision Tasks. In Proceedings of the Adaptive Learning Agents Workshop (at AAMAS), Sao Paulo, Brazil, May 2017.
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2016


  1. Robert Loftin, Matthew E. Taylor, Michael L. Littman, James MacGlashan, Bei Peng, and David L. Roberts. Open Problems for Online Bayesian Inference in Neural Networks. In Proceedings of Bayesian Deep Learning workshop (at NIPS), December 2016.
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  2. Robert Loftin, James MacGlashan, Bei Peng, Matthew E. Taylor, Michael L. Littman, and David L. Roberts. Towards Behavior-Aware Model Learning from Human-Generated Trajectories. In AAAI Fall Symposium on Artificial Intelligence for Human-Robot Interaction, Arlington, VA, USA, November 2016.
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  3. James MacGlashan, Michael L. Littman, David L. Roberts, Robert Loftin, Bei Peng, and Matthew E. Taylor. Convergent Actor Critic by Humans. In Workshop on Human-Robot Collaboration: Towards Co-Adaptive Learning Through Semi-Autonomy and Shared Control (at IROS), October 2016.
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  4. Bei Peng, James MacGlashan, Robert Loftin, Michael L. Littman, David L. Roberts, and Matthew E. Taylor. An Empirical Study of Non-Expert Curriculum Design for Machine Learners. In Proceedings of the Interactive Machine Learning workshop (at IJCAI), New York City, NY, USA, July 2016.
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  5. Bei Peng, James MacGlashan, Robert Loftin, Michael L. Littman, David L. Roberts, and Matthew E. Taylor. A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans. In Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems, May 2016. 24.9% acceptance rate
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2015


  1. Mitchell Scott, Bei Peng, Madeline Chili, Tanay Nigam, Francis Pascual, Cynthia Matuszek, and Matthew E. Taylor. On the Ability to Provide Demonstrations on a UAS: Observing 90 Untrained Participants Abusing a Flying Robot. In Proceedings of the AAAI Fall Symposium on Artificial Intelligence and Human Robot Interaction AI-HRI, November 2015.
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  2. Bei Peng, Robert Loftin, James MacGlashan, Michael L. Littman, Matthew E. Taylor, and David L. Roberts. Language and Policy Learning from Human-delivered Feedback. In Proceedings of the Machine Learning for Social Robotics workshop (ICRA), May 2015.
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  3. Gabriel V. de la Cruz Jr., Bei Peng, Walter S. Lasecki, and Matthew E. Taylor. Towards Integrating Real-Time Crowd Advice with Reinforcement Learning. In The 20th ACM Conference on Intelligent User Interfaces (IUI), March 2015. Poster: 41% acceptance rate for poster submissions
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  4. Gabriel V. de la Cruz Jr., Bei Peng, Walter S. Lasecki, and Matthew E. Taylor. Generating Real-Time Crowd Advice to Improve Reinforcement Learning Agents. In Proceedings of the Learning for General Competency in Video Games workshop (AAAI), January 2015.
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  5. Robert Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, and David L. Roberts. Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning. Journal of autonomous agents and multi-agent systems, pages 1-30, 2015.
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2014


  1. Robert Loftin, Bei Peng, James MacGlashan, Michael Littman, Matthew E. Taylor, David Roberts, and Jeff Huang. Learning Something from Nothing: Leveraging Implicit Human Feedback Strategies. In Proceedings of the 23rd IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), August 2014.
    Nominee for "RSJ/KROS Distinguished Interdisciplinary Research Award" [Download PDF]

  2. Robert Loftin, Bei Peng, James MacGlashan, Michael L. Littman, Matthew E. Taylor, Jeff Huang, and David L. Roberts. A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback. In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), July 2014. 28% acceptance rate
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  3. James Macglashan, Michael L. Littman, Robert Loftin, Bei Peng, David Roberts, and Matthew E. Taylor. Training an Agent to Ground Commands with Reward and Punishment. In Proceedings of the Machine Learning for Interactive Systems workshop (at AAAI-14), July 2014.
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