Biblio

Found 28 results

Filters: First Letter Of Last Name is K [Clear All Filters]

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

V

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.

S

Kelessoglou, M. Theologos, "Simulation and Comparative Evaluation of Flexible Automotive Assembly Layouts", Department of Electrical Engineering and Computer Science, vol. M. Eng., 2016.

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.

Q

Kim, B., L. Bush, and J. A. Shah, "Quantitative Estimation of the Strength of Agreements in Goal-Oriented Meetings", IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 02/2013.

P

Fong, T., J. Scholtz, J. A. Shah, L. Fluckiger, C. Kunz, D. Lees, J. Schreiner, M. Siegel, L. M. Hiatt, I. Nourbakhsh, et al., "A Preliminary Study of Peer-to-Peer Human-Robot Interaction", IEEE International Conference on Systems, Man, and Cybernetics (SMC), 10/2006.

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.

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.

M

Muise, C., S. Wollenstein-Betech, S. Booth, J. Shah, and Y. Khazaen, "Modeling Blackbox Agent Behaviour via Knowledge Compilation", AAAI Conference on Artificial Intelligence, Workshop on Plan, Activity, and Intent Recognition (PAIR), 2020.

Kim, B., J. Shah, and F. Doshi-Velez, "Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction", Neural Information Processing Systems (NIPS), 12/2015.

L

Kim, J., M. E. Woicik, M. C. Gombolay, S-H. Son, and J. A. Shah, "Learning to Infer Final Plans in Human Team Planning", International Joint Conferences on Artificial Intelligence (IJCAI), 07/2018.

I

Kim, B., "Interactive and Interpretable Machine Learning Models for Human Machine Collaboration", Department of Aeronautics and Astronautics, vol. Ph. D., 2015.

Kotowick, K., and J. Shah, "Intelligent Sensory Modality Selection for Electronic Supportive Devices", ACM Conference on Intelligent User Interfaces (IUI), 03/2017.

Kim, B., C. M. Chacha, and J. A. Shah, "Inferring Team Task Plans from Human Meetings: A Generative Modeling Approach with Logic-Based Prior", Journal of Artificial Intelligence Research (JAIR) , pp. 361-398, 2015.

Kim, B., C. M. Chacha, and J. A. Shah, "Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior", AAAI Conference on Artificial Intelligence (AAAI) [29% Acceptance Rate], 07/2013.

Kim, J., "Improving Team's Consistency of Understanding in Meetings: Intelligent Agent Participation and Human Subject Studies", Aeronautics and Astronautics, vol. S.M., Cambridge, MA, Massachusetts Institute of Technology, pp. 66, 2015.

Kim, J., and J. A. Shah, "Improving Team's Consistency of Understanding in Meetings", IEEE Transactions on Human-Machine Systems (THMS), vol. 46, issue 5, pp. 625-637, 04/2016.

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.

E

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.

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.

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.

C

Kim, J., C. J. Banks, and J. A. Shah, "Collaborative Planning with Encoding of Users’ High-level Strategies", AAAI Conference on Artificial Intelligence (AAAI), 02/2017.

B

Shah, A., P. Kamath, S. Li, and J. Shah, "Bayesian Inference of Temporal Task Specifications from Demonstrations", Conference on Neural Information Processing Systems, Montreal, Canada, 2018.

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.

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.

A

Kim, J., and J. A. Shah, "Automatic Prediction of Consistency among Team Members' Understanding of Group Decisions in Meetings", IEEE International Conference on Systems, Man, and Cybernetics (SMC), 10/2014.

Pages