Undergraduate Student
California Institute of Technology
Email: clairechen AT caltech DOT edu

[Google Scholar] [Curriculum Vitae]

Biography

Claire Chen is an undergraduate student at California Institute of Technology (Caltech). Her majors are Mathematics and Computer Science. She was very fortunate to work as a research assistant advised by Professor Nan Jiang, Professor Sergey Levine, Professor Yisong Yue, and Professor Shangtong Zhang. Her research interest is in Reinforcement Learning and LLM Fine-Tuning. She regularly serves on the Program Committee in major AI venues, e.g., ICLR, ICML, NeurIPS.

Conference Publications

• Offline Two-Player Zero-Sum Markov Games with KL Regularization.
  Claire Chen, Yuheng Zhang, Xinyu Liu, Zixuan Xie, Shuze Liu, Nan Jiang.
  International Conference on Machine Learning (ICML), 2026.

• Convergence of Two-Timescale Stochastic Approximation with Markovian Samples and Applications in Reinforcement Learning.
  Vagul Mahadevan, Claire Chen, Shuze Liu, Shangtong Zhang.
  International Conference on Machine Learning (ICML), 2026.

• MathlibLemma: Folklore Lemma Generation and Benchmark for Formal Mathematics.
  Xinyu Liu, Zixuan Xie, Amir Moeini, Claire Chen, Shuze Liu, Yu Meng, Aidong Zhang, Shangtong Zhang.
  International Conference on Machine Learning (ICML), 2026.

• Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning.
  Claire Chen*, Shuze Liu*, Shangtong Zhang.
  International Conference on Learning Representations (ICLR), 2025.

• Doubly Optimal Policy Evaluation for Reinforcement Learning.
  Shuze Liu, Claire Chen, Shangtong Zhang.
  International Conference on Learning Representations (ICLR), 2025.

• Efficient Multi-Policy Evaluation for Reinforcement Learning.
  Shuze Liu, Claire Chen, Shangtong Zhang.
  AAAI Conference on Artificial Intelligence (AAAI), 2025.
  Oral Presentation, Top 4.7%.

Journal Articles

• Optimal Policy Evaluation for Reinforcement Learning.
  Shuze Liu, Claire Chen, Will Ma, Shangtong Zhang.
  Submitted to Operations Research (OR).

Working Papers

• Robust Data-Collection Policy Learning for Low-Variance Online Policy Evaluation.
  Claire Chen, Shuze Liu, Licheng Luo, Rohan Chandra, Nan Jiang, Shangtong Zhang.
  

• Pessimism-Free Offline Learning in General-Sum Games via KL Regularization.
  Claire Chen, Yuheng Zhang.

• Fast Rates in α-Potential Games via Regularized Mirror Descent.
  Claire Chen, Yuheng Zhang.

• AstroAlertBench: Evaluating Vision Language Models for Multimodal Astronomical Alert Triage.
  Claire Chen*, Jiabao Sean Xiao*, Shuze Liu*, Matthew Graham, Ashish Mahabal.

• Beyond Pessimism: Offline Learning in KL-regularized Games.
  Yuheng Zhang, Claire Chen, Nan Jiang.
  

• Instructing LLMs to Negotiate using Reinforcement Learning with Verifiable Rewards.
  Shuze Liu*, Claire Chen*, Jiabao Sean Xiao, Lei Lei, Yisong Yue, David Simchi-Levi.

• Pessimistic Minimax Learning for Public-Private Information Games under Unilateral Coverage.
  Shuze Liu*, Claire Chen*, David Simchi-Levi.

• Transformers Implement Nonlinear In-Context Reinforcement Learning: Convergence and Emergence.
  Zixuan Xie, Xinyu Liu, Claire Chen, Shuze Liu, Rohan Chandra, Shangtong Zhang.
  

Program Committee

ICLR 2025-26, ICML 2025-26, NeurIPS 2026, AAAI 2026, AISTATS 2025-26, AMMAS 2025.

Guest Lecture

Reinforcement Learning from Human Feedback (Fall 2024).

Grant

• Bill Gross Prize for Entrepreneurship ($10,000)
• Thinking Machines Lab Tinker Research Grant.
• Ingrassia Family Research Grant.
• The Bill Gross Prize for Entrepreneurship Finalist.
• CESASC Scholarship
• Caltech PMA Travel Fund.