Biblio

Found 16 results
Author [ Title(Desc)] Type Year
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B
Kim, B., C. Rudin, and J. Shah, "The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification", Neural Information Processing Systems (NIPS), 12/2014.
D
Ramakrishnan, R., E. Kamar, D. Dey, J. Shah, and E. Horvitz, "Discovering Blind Spots in Reinforcement Learning", International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 07/2018.
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.
E
Nikolaidis, S., R. Ramakrishnan, K. Gu, and J. A. Shah, "Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks", ACM/IEEE International Conference on Human Robot Interaction (HRI) [Best Enabling Technology Paper], Portland, Oregon, USA, 03/2015.
Unhelkar, V. V., C. Guan, N. Roy, and J. A. Shah, "Enabling Robot Teammates to Learn Latent States of Human Collaborators", International Conference on Robotics and Automation (ICRA), Workshop on Robot Teammates Operating in Dynamic, Unstructured Environments, 05/2018.
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.
F
Shah, J. A., J. Stedl, B. Williams, and P. Robertson, "A Fast Incremental Algorithm for Maintaining Dispatchability of Partially Controllable Plans", International Conference on Automated Planning and Scheduling (ICAPS) [32% acceptance rate], 09/2007.
I
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.
Nikolaidis, S., P. Lasota, R. Ramakrishnan, and J. Shah, "Improved human–robot team performance through cross-training, an approach inspired by human team training practices", International Journal of Robotics Research (IJRR), vol. 34, issue 14, pp. 1711-1730, 12/2015.
O
Ramakrishnan, R., E. Kamar, B. Nushi, D. Dey, J. Shah, and E. Horvitz, "Overcoming Blind Spots in the Real World: Leveraging Complementary Abilities for Joint Execution", Association for the Advancement of Artificial Intelligence, 01/2019.
P
Ramakrishnan, R., C. Zhang, and J. Shah, "Perturbation Training for Human-Robot Teams", Journal of Artificial Intelligence Research (JAIR), vol. 59, 07/2017.
Ramakrishnan, R., "Perturbation Training for Human-Robot Teams", Department of Electrical Engineering and Computer Science, vol. S.M., 2015.
R
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.
S
Kim, B., K. Patel, A. Rostamizadeh, and J. Shah, "Scalable and interpretable data representation for high-dimensional, complex data", AAAI Conference on Artificial Intelligence (AAAI), 01/2015.
T
Iqbal, T., L. D. Riek, and J. A. Shah, "Toward a Real-time Activity Segmentation Method for Human-Robot Teaming", Robotics: Science and Systems (RSS), Workshop on Towards a framework for Joint Action: What about Theory of Mind?, 06/2018.
Lasota, P. A., G. F. Rossano, and J. A. Shah, "Toward Safe Close-Proximity Human-Robot Interaction with Standard Industrial Robots", IEEE International Conference on Automation Science and Engineering (CASE), 08/2014.