Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication. - Justin Lidard; Udari Madhushani; Naomi Ehrich Leonard (ACC, 2022)

GAME-UP: Game-Aware Mode Enumeration and Understanding for Trajectory Prediction. - Justin Lidard; Oswin So; Yanxia Zhang; Jonathan A. DeCastro; Xiongyi Cui; Xin Huang 018; Yen-Ling Kuo; John J. Leonard; Avinash Balachandran; Naomi Ehrich Leonard; Guy Rosman (CoRR, 2023)

Blending Data-Driven Priors in Dynamic Games. - Justin Lidard; Haimin Hu; Asher Hancock; Zixu Zhang; Albert Gimó Contreras; Vikash Modi; Jonathan A. DeCastro; Deepak Edakkattil Gopinath; Guy Rosman; Naomi Ehrich Leonard; Maria Santos 003; Jaime Fernández Fisac (Robotics: Science and Systems, 2024)

Risk-Calibrated Human-Robot Interaction via Set-Valued Intent Prediction. - Justin Lidard; Hang Pham; Ariel Bachman; Bryan Boateng; Anirudha Majumdar (Robotics: Science and Systems, 2024)

A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions. - Ola Shorinwa; Zhiting Mei; Justin Lidard; Allen Z. Ren; Anirudha Majumdar (CoRR, 2024)

Diffusion Policy Policy Optimization. - Allen Z. Ren; Justin Lidard; Lars Lien Ankile; Anthony Simeonov; Pulkit Agrawal 001; Anirudha Majumdar; Benjamin Burchfiel; Hongkai Dai; Max Simchowitz (ICLR, 2025)

Safety with Agency: Human-Centered Safety Filter with Application to AI-Assisted Motorsports. - Donggeon David Oh; Justin Lidard; Haimin Hu; Himani Sinhmar; Elle Lazarski; Deepak Edakkattil Gopinath; Emily S. Sumner; Jonathan A. DeCastro; Guy Rosman; Naomi Ehrich Leonard; Jaime Fernández Fisac (CoRR, 2025)

Guiding Data Collection via Factored Scaling Curves. - Lihan Zha; Apurva Badithela; Michael Zhang; Justin Lidard; Jeremy Bao; Emily Zhou; David Snyder; Allen Z. Ren; Dhruv Shah; Anirudha Majumdar (CoRR, 2025)

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know? - Zhiting Mei; Christina Zhang; Tenny Yin; Justin Lidard; Ola Shorinwa; Anirudha Majumdar (CoRR, 2025)