Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning. - Tu Trinh; Daniel S. Brown (CoRR, 2022)

Efficient Game-Theoretic Planning With Prediction Heuristic for Socially-Compliant Autonomous Driving. - Chenran Li; Tu Trinh; Letian Wang; Changliu Liu; Masayoshi Tomizuka; Wei Zhan (IEEE Robotics Autom. Lett., 2022)

Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning. - Tu Trinh; Haoyu Chen; Daniel S. Brown (HRI, 2024)

A StrongREJECT for Empty Jailbreaks. - Alexandra Souly; Qingyuan Lu; Dillon Bowen; Tu Trinh; Elvis Hsieh; Sana Pandey; Pieter Abbeel; Justin Svegliato; Scott Emmons; Olivia Watkins; Sam Toyer (NeurIPS, 2024)

Softmax Probabilities (Mostly) Predict Large Language Model Correctness on Multiple-Choice Q&A. - Benjamin Plaut; Khanh Nguyen; Tu Trinh (CoRR, 2024)

Refusal-Trained LLMs Are Easily Jailbroken As Browser Agents. - Priyanshu Kumar; Elaine Lau; Saranya Vijayakumar; Tu Trinh; Scale Red Team; Elaine T. Chang; Vaughn Robinson; Sean Hendryx; Shuyan Zhou; Matt Fredrikson; Summer Yue; Zifan Wang 001 (CoRR, 2024)

Getting By Goal Misgeneralization With a Little Help From a Mentor. - Tu Trinh; Mohamad H. Danesh; Nguyen X. Khanh; Benjamin Plaut (CoRR, 2024)

Aligned LLMs Are Not Aligned Browser Agents. - Priyanshu Kumar; Elaine Lau; Saranya Vijayakumar; Tu Trinh; Elaine T. Chang; Vaughn Robinson; Shuyan Zhou; Matt Fredrikson; Sean M. Hendryx; Summer Yue; Zifan Wang 001 (ICLR, 2025)

Probabilities of Chat LLMs Are Miscalibrated but Still Predict Correctness on Multiple-Choice Q&A. - Benjamin Plaut; Nguyen X. Khanh; Tu Trinh (Trans. Mach. Learn. Res., 2025)

Learning to Coordinate with Experts. - Mohamad H. Danesh; Tu Trinh; Benjamin Plaut; Nguyen X. Khanh (CoRR, 2025)