Yao Liu

According to our database, Yao Liu authored at least 27 papers between 2016 and 2025.

Collaborative distances:

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

2025

Teaching Large Language Models to Reason through Learning and Forgetting.

Tianwei Ni, Allen Nie, Sapana Chaudhary, Yao Liu, Huzefa Rangwala, Rasool Fakoor

CoRR, April, 2025

From Demonstrations to Rewards: Alignment Without Explicit Human Preferences.

Siliang Zeng, Yao Liu, Huzefa Rangwala, George Karypis, Mingyi Hong, Rasool Fakoor

CoRR, March, 2025

Offline Learning and Forgetting for Reasoning with Large Language Models.

Tianwei Ni, Allen Nie, Sapana Chaudhary, Yao Liu, Huzefa Rangwala, Rasool Fakoor

Trans. Mach. Learn. Res., 2025

Bridging the Training-Inference Gap in LLMs by Leveraging Self-Generated Tokens.

Zhepeng Cen, Yao Liu, Siliang Zeng, Pratik Chaudhari, Huzefa Rangwala, George Karypis, Rasool Fakoor

Trans. Mach. Learn. Res., 2025

AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents.

Ke Yang, Yao Liu, Sapana Chaudhary, Rasool Fakoor, Pratik Chaudhari, George Karypis, Huzefa Rangwala

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Reinforcement learning tutor better supported lower performers in a math task.

Sherry Ruan, Allen Nie, William Steenbergen, Jiayu He, J. Q. Zhang, Meng Guo, Yao Liu, Kyle Dang Nguyen, Catherine Y. Wang, Rui Ying, James A. Landay, Emma Brunskill

Mach. Learn., May, 2024

EXTRACT: Efficient Policy Learning by Extracting Transferrable Robot Skills from Offline Data.

Jesse Zhang, Minho Heo, Zuxin Liu, Erdem Biyik, Joseph J. Lim, Yao Liu, Rasool Fakoor

CoRR, 2024

Learning the Target Network in Function Space.

Kavosh Asadi, Yao Liu, Shoham Sabach, Ming Yin, Rasool Fakoor

Proceedings of the Forty-first International Conference on Machine Learning, 2024

TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models.

Zuxin Liu, Jesse Zhang, Kavosh Asadi, Yao Liu, Ding Zhao, Shoham Sabach, Rasool Fakoor

Proceedings of the Twelfth International Conference on Learning Representations, 2024

EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data.

Jesse Zhang, Minho Heo, Zuxin Liu, Erdem Biyik, Joseph J. Lim, Yao Liu, Rasool Fakoor

Proceedings of the Conference on Robot Learning, 6-9 November 2024, Munich, Germany., 2024

2023

Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task.

Sherry Ruan, Allen Nie, William Steenbergen, Jiayu He, JQ Zhang, Meng Guo, Yao Liu, Kyle Dang Nguyen, Catherine Y. Wang, Rui Ying, James A. Landay, Emma Brunskill

CoRR, 2023

TD Convergence: An Optimization Perspective.

Kavosh Asadi, Shoham Sabach, Yao Liu, Omer Gottesman, Rasool Fakoor

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Budgeting Counterfactual for Offline RL.

Yao Liu, Pratik Chaudhari, Rasool Fakoor

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Offline policy optimization with eligible actions.

Yao Liu, Yannis Flet-Berliac, Emma Brunskill

Proceedings of the Uncertainty in Artificial Intelligence, 2022

2021

Adaptive and efficient batch reinforcement learning algorithms.

Yao Liu

PhD thesis, 2021

2020

Provably Good Batch Reinforcement Learning Without Great Exploration.

Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

CoRR, 2020

Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration.

Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions.

Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo A. Celi, Emma Brunskill, Finale Doshi-Velez

Proceedings of the 37th International Conference on Machine Learning, 2020

Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling.

Yao Liu, Pierre-Luc Bacon, Emma Brunskill

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

All-Action Policy Gradient Methods: A Numerical Integration Approach.

Benjamin Petit, Loren Amdahl-Culleton, Yao Liu, Jimmy Smith, Pierre-Luc Bacon

CoRR, 2019

Off-Policy Policy Gradient with State Distribution Correction.

Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

CoRR, 2019

Off-Policy Policy Gradient with Stationary Distribution Correction.

Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Combining parametric and nonparametric models for off-policy evaluation.

Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters.

Aniruddh Raghu, Omer Gottesman, Yao Liu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, Emma Brunskill

CoRR, 2018

When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms.

Yao Liu, Emma Brunskill

CoRR, 2018

Representation Balancing MDPs for Off-policy Policy Evaluation.

Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo A. Faisal, Finale Doshi-Velez, Emma Brunskill

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2016

PAC Continuous State Online Multitask Reinforcement Learning with Identification.

Yao Liu, Zhaohan Guo, Emma Brunskill

Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, 2016