From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood. - Kelvin Guu; Panupong Pasupat; Evan Zheran Liu; Percy Liang (ACL (1), 2017)

Mapping natural language commands to web elements. - Panupong Pasupat; Tian-Shun Jiang; Evan Zheran Liu; Kelvin Guu; Percy Liang (EMNLP, 2018)

Reinforcement Learning on Web Interfaces using Workflow-Guided Exploration. - Evan Zheran Liu; Kelvin Guu; Panupong Pasupat; Tianlin Shi; Percy Liang (ICLR (Poster), 2018)

An Imitation Learning Approach for Cache Replacement. - Evan Zheran Liu; Milad Hashemi; Kevin Swersky; Parthasarathy Ranganathan; Junwhan Ahn (ICML, 2020)

Learning Abstract Models for Strategic Exploration and Fast Reward Transfer. - Evan Zheran Liu; Ramtin Keramati; Sudarshan Seshadri; Kelvin Guu; Panupong Pasupat; Emma Brunskill; Percy Liang (CoRR, 2020)

Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning. - Evan Zheran Liu; Aditi Raghunathan; Percy Liang; Chelsea Finn (CoRR, 2020)

Just Train Twice: Improving Group Robustness without Training Group Information. - Evan Zheran Liu; Behzad Haghgoo; Annie S. Chen; Aditi Raghunathan; Pang Wei Koh; Shiori Sagawa; Percy Liang; Chelsea Finn (ICML, 2021)

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices. - Evan Zheran Liu; Aditi Raghunathan; Percy Liang; Chelsea Finn (ICML, 2021)

Analyzing a Caching Model. - Leon Sixt; Evan Zheran Liu; Marie Pellat; James Wexler; Milad Hashemi; Been Kim; Martin Maas 001 (CoRR, 2021)

Learning Options via Compression. - Yiding Jiang; Evan Zheran Liu; Benjamin Eysenbach; J. Zico Kolter; Chelsea Finn (NeurIPS, 2022)

Giving Feedback on Interactive Student Programs with Meta-Exploration. - Evan Zheran Liu; Moritz Stephan; Allen Nie; Chris Piech; Emma Brunskill; Chelsea Finn (NeurIPS, 2022)

Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning. - Evan Zheran Liu; Sahaana Suri; Tong Mu; Allan Zhou; Chelsea Finn (ICML, 2023)

A Survey of Meta-Reinforcement Learning. - Jacob Beck; Risto Vuorio; Evan Zheran Liu; Zheng Xiong; Luisa M. Zintgraf; Chelsea Finn; Shimon Whiteson (CoRR, 2023)

Learning to Explore in POMDPs with Informational Rewards. - Annie Xie; Logan M. Bhamidipaty; Evan Zheran Liu; Joey Hong; Sergey Levine; Chelsea Finn (ICML, 2024)

A Fast and Accurate Machine Learning Autograder for the Breakout Assignment. - Evan Zheran Liu; David Yuan 001; Ahmed Ahmed; Elyse Cornwall; Juliette Woodrow; Kaylee Burns; Allen Nie; Emma Brunskill; Chris Piech; Chelsea Finn (SIGCSE (1), 2024)

AutoBencher: Creating Salient, Novel, Difficult Datasets for Language Models. - Xiang Lisa Li; Evan Zheran Liu; Percy Liang; Tatsunori Hashimoto (CoRR, 2024)

AutoBencher: Towards Declarative Benchmark Construction. - Xiang Lisa Li; Farzaan Kaiyom; Evan Zheran Liu; Yifan Mai 001; Percy Liang; Tatsunori Hashimoto (ICLR, 2025)

A Tutorial on Meta-Reinforcement Learning. - Jacob Beck; Risto Vuorio; Evan Zheran Liu; Zheng Xiong; Luisa M. Zintgraf; Chelsea Finn; Shimon Whiteson (Found. Trends Mach. Learn., 2025)