PlanIt: A crowdsourcing approach for learning to plan paths from large scale preference feedback. - Ashesh Jain; Debarghya Das; Jayesh K. Gupta; Ashutosh Saxena (ICRA, 2015)

Model-Free Imitation Learning with Policy Optimization. - Jonathan Ho; Jayesh K. Gupta; Stefano Ermon (ICML, 2016)

Cooperative Multi-agent Control Using Deep Reinforcement Learning. - Jayesh K. Gupta; Maxim Egorov; Mykel J. Kochenderfer (AAMAS Workshops (Selected Papers), 2017)

Layer-wise synapse optimization for implementing neural networks on general neuromorphic architectures. - John Mern; Jayesh K. Gupta; Mykel J. Kochenderfer (SSCI, 2017)

POMDPs.jl: A Framework for Sequential Decision Making under Uncertainty. - Maxim Egorov; Zachary N. Sunberg; Edward Balaban; Tim Allan Wheeler; Jayesh K. Gupta; Mykel J. Kochenderfer (J. Mach. Learn. Res., 2017)

Evaluating Generalization in Multiagent Systems using Agent-Interaction Graphs. - Aditya Grover; Maruan Al-Shedivat; Jayesh K. Gupta; Yuri Burda; Harrison Edwards (AAMAS, 2018)

Learning Policy Representations in Multiagent Systems. - Aditya Grover; Maruan Al-Shedivat; Jayesh K. Gupta; Yuri Burda; Harrison Edwards (ICML, 2018)

Model Primitive Hierarchical Lifelong Reinforcement Learning. - Bohan Wu; Jayesh K. Gupta; Mykel J. Kochenderfer (AAMAS, 2019)

Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning. - Raunak P. Bhattacharyya; Derek J. Phillips; Changliu Liu; Jayesh K. Gupta; Katherine Rose Driggs-Campbell; Mykel J. Kochenderfer (ICRA, 2019)

A General Framework for Structured Learning of Mechanical Systems. - Jayesh K. Gupta; Kunal Menda; Zachary Manchester; Mykel J. Kochenderfer (CoRR, 2019)

Health-Informed Policy Gradients for Multi-Agent Reinforcement Learning. - Ross E. Allen; Javona White Bear; Jayesh K. Gupta; Mykel J. Kochenderfer (CoRR, 2019)

Normalizing Flow Model for Policy Representation in Continuous Action Multi-agent Systems. - Xiaobai Ma; Jayesh K. Gupta; Mykel J. Kochenderfer (AAMAS, 2020)

Normalizing Flow Policies for Multi-agent Systems. - Xiaobai Ma; Jayesh K. Gupta; Mykel J. Kochenderfer (GameSec, 2020)

Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM. - Kunal Menda; Jean de Becdelièvre; Jayesh K. Gupta; Ilan Kroo; Mykel J. Kochenderfer; Zachary Manchester (ICML, 2020)

Structured Mechanical Models for Robot Learning and Control. - Jayesh K. Gupta; Kunal Menda; Zachary Manchester; Mykel J. Kochenderfer (L4DC, 2020)

Model primitives for hierarchical lifelong reinforcement learning. - Bohan Wu; Jayesh K. Gupta; Mykel J. Kochenderfer (Auton. Agents Multi Agent Syst., 2020)

Modularity and coordination for planning and reinforcement learning. - Jayesh K. Gupta (2020)

Normalized Stanford Helicopter Dataset. - Kunal Menda; Jean de Becdelièvre; Jayesh K. Gupta; Ilan Kroo; Mykel John Kochenderfer; Zachary Manchester (Zenodo, 2020)

Scalable Anytime Planning for Multi-Agent MDPs. - Shushman Choudhury; Jayesh K. Gupta; Peter Morales; Mykel J. Kochenderfer (AAMAS, 2021)

Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning. - Sheng Li; Jayesh K. Gupta; Peter Morales; Ross E. Allen; Mykel J. Kochenderfer (AAMAS, 2021)

Training Structured Mechanical Models by Minimizing Discrete Euler-Lagrange Residual. - Kunal Menda; Jayesh K. Gupta; Zachary Manchester; Mykel J. Kochenderfer (CoRR, 2021)

Recursive Reasoning Graph for Multi-Agent Reinforcement Learning. - Xiaobai Ma; David Isele; Jayesh K. Gupta; Kikuo Fujimura; Mykel J. Kochenderfer (AAAI, 2022)

Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement Learning. - Jennifer She; Jayesh K. Gupta; Mykel J. Kochenderfer (AAMAS, 2022)

Scalable Anytime Planning for Multi-Agent MDPs (Extended Abstract). - Shushman Choudhury; Jayesh K. Gupta; Mykel J. Kochenderfer (IJCAI, 2022)

Learning to Simulate Realistic LiDARs. - Benoît Guillard; Sai Vemprala; Jayesh K. Gupta; Ondrej Miksik; Vibhav Vineet; Pascal Fua; Ashish Kapoor (IROS, 2022)

COMPASS: Contrastive Multimodal Pretraining for Autonomous Systems. - Shuang Ma; Sai Vemprala; Wenshan Wang; Jayesh K. Gupta; Yale Song; Daniel McDuff; Ashish Kapoor (IROS, 2022)

Learning Modular Simulations for Homogeneous Systems. - Jayesh K. Gupta; Sai Vemprala; Ashish Kapoor (NeurIPS, 2022)

Dynamic multi-robot task allocation under uncertainty and temporal constraints. - Shushman Choudhury; Jayesh K. Gupta; Mykel J. Kochenderfer; Dorsa Sadigh; Jeannette Bohg (Auton. Robots, 2022)

Scalable Online Planning for Multi-Agent MDPs. - Shushman Choudhury; Jayesh K. Gupta; Peter Morales; Mykel J. Kochenderfer (J. Artif. Intell. Res., 2022)

Clifford Neural Layers for PDE Modeling. - Johannes Brandstetter; Rianne van den Berg; Max Welling; Jayesh K. Gupta (ICLR, 2023)

ClimaX: A foundation model for weather and climate. - Tung Nguyen; Johannes Brandstetter; Ashish Kapoor; Jayesh K. Gupta; Aditya Grover (ICML, 2023)

Geometric Clifford Algebra Networks. - David Ruhe; Jayesh K. Gupta; Steven De Keninck; Max Welling; Johannes Brandstetter (ICML, 2023)

Towards Multi-spatiotemporal-scale Generalized PDE Modeling. - Jayesh K. Gupta; Johannes Brandstetter (Trans. Mach. Learn. Res., 2023)

EvDNeRF: Reconstructing Event Data with Dynamic Neural Radiance Fields. - Anish Bhattacharya; Ratnesh Madaan; Fernando Cladera Ojeda; Sai Vemprala; Rogerio Bonatti; Kostas Daniilidis; Ashish Kapoor; Vijay Kumar 001; Nikolai Matni; Jayesh K. Gupta (WACV, 2024)

Aurora: A Foundation Model of the Atmosphere. - Cristian Bodnar; Wessel P. Bruinsma; Ana Lucic; Megan Stanley; Johannes Brandstetter; Patrick Garvan; Maik Riechert; Jonathan A. Weyn; Haiyu Dong; Anna Vaughan; Jayesh K. Gupta; Kit Thambiratnam; et al. (CoRR, 2024)

A foundation model for the Earth system. - Cristian Bodnar; Wessel P. Bruinsma; Ana Lucic; Megan Stanley; Anna Allen; Johannes Brandstetter; Patrick Garvan; Maik Riechert; Jonathan A. Weyn; Haiyu Dong; Jayesh K. Gupta; Kit Thambiratnam; et al. (Nat., 2025)