Graph-Structured Visual Imitation. - Maximilian Sieb; Xian Zhou; Audrey Huang; Oliver Kroemer; Katerina Fragkiadaki (CoRL, 2019)

Off-Policy Risk Assessment in Contextual Bandits. - Audrey Huang; Liu Leqi; Zachary C. Lipton; Kamyar Azizzadenesheli (NeurIPS, 2021)

On the Convergence and Optimality of Policy Gradient for Markov Coherent Risk. - Audrey Huang; Liu Leqi; Zachary C. Lipton; Kamyar Azizzadenesheli (CoRR, 2021)

Off-Policy Risk Assessment for Markov Decision Processes. - Audrey Huang; Liu Leqi; Zachary C. Lipton; Kamyar Azizzadenesheli (AISTATS, 2022)

Offline Reinforcement Learning with Realizability and Single-policy Concentrability. - Wenhao Zhan; Baihe Huang; Audrey Huang; Nan Jiang 008; Jason D. Lee (COLT, 2022)

Supervised Learning with General Risk Functionals. - Liu Leqi; Audrey Huang; Zachary C. Lipton; Kamyar Azizzadenesheli (ICML, 2022)

Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions. - Audrey Huang; Nan Jiang 008 (NeurIPS, 2022)

Extended Abstract: Learning in Low-rank MDPs with Density Features. - Audrey Huang; Jinglin Chen; Nan Jiang 008 (CISS, 2023)

Reinforcement Learning in Low-rank MDPs with Density Features. - Audrey Huang; Jinglin Chen; Nan Jiang 008 (ICML, 2023)

Timing as an Action: Learning When to Observe and Act. - Helen Zhou; Audrey Huang; Kamyar Azizzadenesheli; David Childers; Zachary C. Lipton (AISTATS, 2024)

Occupancy-based Policy Gradient: Estimation, Convergence, and Optimality. - Audrey Huang; Nan Jiang 008 (NeurIPS, 2024)

Non-adaptive Online Finetuning for Offline Reinforcement Learning. - Audrey Huang; Mohammad Ghavamzadeh; Nan Jiang 008; Marek Petrik (RLJ, 2024)

Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification (extended abstract). - Dhruv Rohatgi; Adam Block; Audrey Huang; Akshay Krishnamurthy; Dylan J. Foster (COLT, 2025)

Self-Improvement in Language Models: The Sharpening Mechanism. - Audrey Huang; Adam Block; Dylan J. Foster; Dhruv Rohatgi; Cyril Zhang; Max Simchowitz; Jordan T. Ash; Akshay Krishnamurthy (ICLR, 2025)

Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization. - Audrey Huang; Wenhao Zhan; Tengyang Xie; Jason D. Lee; Wen Sun 002; Akshay Krishnamurthy; Dylan J. Foster (ICLR, 2025)

Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol. - Pai Liu; Lingfeng Zhao; Shivangi Agarwal; Jinghan Liu; Audrey Huang; Philip Amortila; Nan Jiang 008 (CoRR, 2025)

Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification. - Dhruv Rohatgi; Adam Block; Audrey Huang; Akshay Krishnamurthy; Dylan J. Foster (CoRR, 2025)

Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment. - Audrey Huang; Adam Block; Qinghua Liu; Nan Jiang 008; Dylan J. Foster; Akshay Krishnamurthy (CoRR, 2025)