Biblio

Found 135 results
Author Title Type [ Year(Asc)]
2023
Booth, S., B. W. Knox, J. Shah, S. Niekum, P. Stone, and A. Allievi, "The Perils of Trial-and-Error Reward Design: Misdesign through Overfitting and Invalid Task Specifications", Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), Washington, D.C. , 02/2023.
Zhou, Y., and J. Shah, "The Solvability of Interpretability Evaluation Metrics", Findings of the Association for Computational Linguistics: EACL: Association for Computational Linguistics, 05/2023.
Horter, T., E. L. Glassman, J. Shah, and S. Booth, "Varying How We Teach: Adding Contrast Helps Humans Learn about Robot Motions", ACM/IEEE International Conference on Human-Robot Interaction (HRI), Human-Interactive Robot Learning (HIRL) , 2023.
2022
Zhou, Y., S. Booth, M. Tulio Ribeiro, and J. Shah, "Do Feature Attribution Methods Correctly Attribute Features?", Proceedings of the 36th AAAI Conference on Artificial Intelligence: AAAI, 02/2022.
Zhou, Y., M. Tulio Ribeiro, and J. Shah, "ExSum: From Local Explanations to Model Understanding", Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): Association for Computational Linguistics, 07/2022.
Booth, S., W. B. Knox, J. Shah, S. Niekum, P. Stone, and A. Allievi, "Extended Abstract: Graduate Student Descent Considered Harmful? A Proposal for Studying Overfitting in Reward Functions", The Multi-disciplinary Conference on Reinforcement Learning and Decision Making, Providence, RI, 2022.
Knox, W. B., S. Hatgis-Kessell, S. Booth, S. Niekum, P. Stone, and A. Allievi, "Extended Abstract: Partial Return Poorly Explains Human Preferences", The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), Providence, RI, 2022.
Zheng, Y., S. Booth, J. Shah, and Y. Zhou, "The Irrationality of Neural Rationale Models", Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2nd Workshop on Trustworthy Natural Langauge Processing (TrustNLP), 07/2022.
Booth, S., S. Sharma, S. Chung, J. Shah, and E. L. Glassman, "Revisiting Human-Robot Teaching and Learning Through the Lens of Human Concept Learning Theory", ACM/IEEE International Conference on Human-Robot Interaction (HRI), 03/2022.
Li*, S., T. Stouraitis*, M. Gienger, S. Vijayakumar, and J. A. Shah, "Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty", IEEE Robotics and Automation Letters (RA-L), vol. 7, issue 3, 03/2022.
Wang, Y., N. Figueroa, S. Li, A. Shah, and J. Shah, "Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations", 6th Annual Conference on Robot Learning, Auckland, New Zealand, 12/2022.
Wang, Y., C-Y. Ko, and P. Agrawal, "Visual Pre-training for Navigation: What Can We Learn from Noise?", Thirty-sixth Annual Conference on Neural Information Processing Systems, New Orleans, LA, Synthetic Data for Empowering ML Research Workshop & Self-Supervised Learning Workshop, 2022.
2021
Booth*, S., Y. Zhou*, A. Shah, and J. Shah, "Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example", AAAI Conference on Artificial Intelligence, 2021.
Booth, S., S. Sharma, S. Chung, J. Shah, and E. L. Glassman, "How to Understand Your Robot: A Design Space Informed by Human Concept Learning", International Conference on Robotics and Automation (ICRA), Workshop on Social Intelligence in Humans and Robots (SIHR), 05/2021.
Winfield, A. F. T., S. Booth, L. A. Dennis, T. Egawa, H. Hastie, N. Jacobs, R. I. Muttram, J. I. Olszewska, F. Rajabiyazdi, A. Theodorou, et al., "IEEE P7001: a proposed standard on transparency", Frontiers in Robotics and AI, pp. 225, 2021.
Kim, D., "Imitation Learning for Sequential Manipulation Tasks: Leveraging Language and Perception", Department of Electrical Engineering and Computer Science, vol. M. Eng: Massachusetts Institute of Technology, 2021.
Hopkins*, A., and S. Booth*, "Machine learning practices outside big tech: How resource constraints challenge responsible development", Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 2021.
Li, S., N. Figueroa, A. Shah, and J. A. Shah, "Provably Safe and Efficient Motion Planning with Uncertain Human Dynamics", Robotics: Science and Systems (R:SS), 07/2021.
Li*, S., D. Park*, Y. Sung*, J. Shah, and N. Roy, "Reactive Task and Motion Planning under Temporal Logic Specifications", IEEE International Conference on Robotics and Automation (ICRA), 06/2021.
Zhou, Y., S. Booth, N. Figueroa, and J. Shah, "RoCUS: Robot Controller Understanding via Sampling", Conference on Robot Learning (CoRL), London, UK, Proceedings of Machine Learning Research, 11/2021.
2019
Lasota, P. A., and J. A. Shah, "Bayesian Estimator for Partial Trajectory Alignment", Robotics: Science and Systems [31% Acceptance Rate], 06/2019.
Kim, J., C. Muise, A. Shah, S. Agarwal, and J. Shah, "Bayesian Inference of Linear Temporal Logic Specifications for Contrastive Explanations", International Joint Conference on Artificial Intelligence (IJCAI), Macau, China, 08/2019.

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