Justin Lidard

According to our database, Justin Lidard authored at least 9 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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, June, 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, May, 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, April, 2025

Diffusion Policy Policy Optimization.

Allen Z. Ren, Justin Lidard, Lars Lien Ankile, Anthony Simeonov, Pulkit Agrawal, Anirudha Majumdar, Benjamin Burchfiel, Hongkai Dai, Max Simchowitz

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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

Risk-Calibrated Human-Robot Interaction via Set-Valued Intent Prediction.

Justin Lidard, Hang Pham, Ariel Bachman, Bryan Boateng, Anirudha Majumdar

Proceedings of the Robotics: Science and Systems XX, 2024

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, Jaime Fernández Fisac

Proceedings of the Robotics: Science and Systems XX, 2024

2023

GAME-UP: Game-Aware Mode Enumeration and Understanding for Trajectory Prediction.

Justin Lidard, Oswin So, Yanxia Zhang, Jonathan A. DeCastro, Xiongyi Cui, Xin Huang, Yen-Ling Kuo, John J. Leonard, Avinash Balachandran, Naomi Ehrich Leonard, Guy Rosman

CoRR, 2023

2022

Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication.

Justin Lidard, Udari Madhushani, Naomi Ehrich Leonard

Proceedings of the American Control Conference, 2022