Biblio

Found 15 results
Author Title Type [ Year(Asc)]
Filters: First Letter Of Last Name is R  [Clear All Filters]
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.
2018
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.
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.
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.
2017
Ramakrishnan, R., C. Zhang, and J. Shah, "Perturbation Training for Human-Robot Teams", Journal of Artificial Intelligence Research (JAIR), vol. 59, 07/2017.
2015
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.
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.
Ramakrishnan, R., "Perturbation Training for Human-Robot Teams", Department of Electrical Engineering and Computer Science, vol. S.M., 2015.
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.
2014
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.
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.
2007
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.