About me

I am an applied scientist at Amazon working on reinforcement learning. My research goal is creating AI agents that are capable of making decisions in unknown and complex environments, besides perception and prediction. Such problems are often formalized under the reinforcement learning (RL) framework. I am interested in understanding and developing RL algorithms from the first principles, with a focus on causality [1], scalability [2,3], and value function learning dynamics [4,5] in RL. Besides the fundamental RL research, I work on user alignment of foundation models (Amazon Bedrock).

Previously, I was a research scientist at ByteDance from 2021 to 2022. I worked on applications of bandit and RL in TikTok and Douyin video recommendation. I obtained my Ph.D. in computer science from Stanford in 2021, advised by Emma Brunskill. My dissertation work is about batch reinforcement learning. With my collaborators, we proposed the first finite sample error bound of batch RL without full coverage assumption and convergent batch policy gradient with function approximation. I also worked on batch RL applications in the real world: helping chronic condition patients, treatment policy evaluation on clinical data with practicing intensivist, and teaching kids math. I complete my B.S. in machine intelligence from Peking University in 2016.

Preprints and Publications

• Bridging the Training-Inference Gap in LLMs by Leveraging Self-Generated Tokens
  Zhepeng Cen, Yao Liu, Siliang Zeng, Pratik Chaudhar, Huzefa Rangwala, George Karypis, Rasool Fakoor
  Preprint arXiv
  
• 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
  Preprint arXiv
  
• 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
  CoRL 2024 arXiv
  
• Learning the Target Network in Function Space
  Kavosh Asadi*, Yao Liu*, Shoham Sabach*, Yin Ming*, Rasool Fakoor
  ICML 2024 arXiv
  
• 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
  ICLR 2024 arXiv
  
• Budgeting Counterfactual for Offline RL
  Yao Liu, Pratik Chaudhari, Rasool Fakoor
  NeurIPS 2023 arXiv
  
• TD Convergence: An Optimization Perspective
  Kavosh Asadi, Shoham Sabach, Yao Liu, Omer Gottesman, Rasool Fakoor
  NeurIPS 2023 arXiv
  
• 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
  Machine Learning Journal Paper arXiv
  
• Provably Sample-Efficient RL with Side Information about Latent Dynamics
  Yao Liu, Dipendra Misra, Miro Dudík, Robert E. Schapire.
  NeurIPS 2022 arXiv
  
• Offline Policy Optimization with Eligible Actions
  Yao Liu, Yannis Flet-Berliac, Emma Brunskill.
  UAI 2022 arXiv
  
• Provably Good Batch Reinforcement Learning Without Great Exploration
  Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill.
  NeurIPS 2020 arXiv Code Talk
  
• Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling
  Yao Liu, Pierre-Luc Bacon, Emma Brunskill
  ICML 2020 arXiv Talk
  
• Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
  Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez
  ICML 2020 arXiv Code
  
• All-Action Policy Gradient Methods: A Numerical Integration Approach
  Benjamin Petit, Loren Amdahl-Culleton, Yao Liu, Jimmy Smith, Pierre-Luc Bacon arXiv
  
• Off-Policy Policy Gradient with State Distribution Correction
  Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
  UAI 2019 (Oral) Supplemental arXiv
• Combining Parametric and Nonparametric Models for Off-Policy Evaluation
  Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez
  ICML 2019 (Oral) arXiv Slides
• Representation Balancing MDPs for Off-Policy Policy Evaluation
  Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, Emma Brunskill.
  NeurIPS 2018 arXiv Code Talk
  
• When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms
  Yao Liu, Emma Brunskill.
  EWRL 2018 arXiv
  
• Behaviour Policy Estimation in Off-Policy Evaluation: Calibration Matters
  Aniruddh Raghu, Omer Gottesman,Yao Liu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, Emma Brunskill
  ICML 2018 Workshops
  
• Switched Trajectories for Off-Policy Learning
  Scott Sussex , Omer Gottesman,Yao Liu, Susan Murphy, Emma Brunskill, Finale Doshi-Velez
  ICML 2018 Workshops
  
• Model Selection for Off-Policy Policy Evaluation
  Yao Liu, Philip S. Thomas, Emma Brunskill
  RLDM 2017, Extended Abstract
  
• PAC Continuous State Online Multitask Reinforcement Learning with Identification
  Yao Liu, Zhaohan Guo, Emma Brunskill
  AAMAS 2016 Supplemental
  
• Local Orthogonality Preserving Alignment for Nonlinear Dimensionality Reduction
  Tong Lin, Yao Liu, Bo Wang, Liwei Wang, Hongbin Zha
  Journal of Computer Science and Technology, 31(3): 512-524, 2016. Code
  

Professional Service

Journal Reviewing: JMLR, IEEE TPAMI, Machine Learning, Artificial Intelligence, Biometrika

Conference Reviewing: NeurIPS, ICLR, ICML, AISTATS, UAI, AAAI