About Me

I am Yulai, a second-year Ph.D. candidate at Princeton working on machine learning.

My research primarily explores the theories and empirical applications of reinforcement learning. I am particularly interested in the vast possibilities that data-driven learning paradigm can unlock in scientific studies.

Experiences

  • Research Intern @ BRAID (Biology Research | AI Development), Research & Early Development, Genentech
  • Ph.D. Student @ Electrical and Computer Engineering, Princeton University
  • Visiting Student @ Institute for Machine Learning, ETH Zürich
  • Research Assistant @ Computer Science & Engineering, University of Washington
  • Bachelor @ Electronic Engineering, Tsinghua University

Publications

* denotes equal contribution or alphabetical ordering.

  • Adding Conditional Control to Diffusion Models with Reinforcement Learning
    Yulai Zhao*, Masatoshi Uehara*, Gabriele Scalia, Tommaso Biancalani, Sergey Levine, Ehsan Hajiramezanali
    [arXiv]

  • Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models
    Masatoshi Uehara*, Yulai Zhao*, Ehsan Hajiramezanali, Gabriele Scalia, Gökcen Eraslan, Avantika Lal, Sergey Levine, Tommaso Biancalani
    [arXiv]

  • Feedback Efficient Online Fine-Tuning of Diffusion Models
    Masatoshi Uehara*, Yulai Zhao*, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M Tseng, Sergey Levine, Tommaso Biancalani
    International Conference on Machine Learning (ICML) 2024
    [paper] [arXiv]

  • Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control
    Masatoshi Uehara*, Yulai Zhao*, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M Tseng, Tommaso Biancalani, Sergey Levine
    [arXiv]

  • Provably Efficient CVaR RL in Low-rank MDPs
    Yulai Zhao*, Wenhao Zhan*, Xiaoyan Hu*, Ho-fung Leung, Farzan Farnia, Wen Sun, Jason D. Lee
    International Conference on Learning Representations (ICLR) 2024
    [paper] [arXiv]

  • Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning
    Yulai Zhao, Zhuoran Yang, Zhaoran Wang, Jason D. Lee
    International Conference on Machine Learning (ICML) 2023
    [paper] [arXiv]

  • Blessing of Class Diversity in Pre-training
    Yulai Zhao, Jianshu Chen, Simon S. Du
    International Conference on Artificial Intelligence and Statistics (AISTATS) 2023
    [Oral Presentation] [Notable Paper]
    [paper] [arXiv]

  • Optimizing the Performative Risk under Weak Convexity Assumptions
    Yulai Zhao
    NeurIPS 2022 Workshop on Optimization for Machine Learning
    [paper] [arXiv]

  • Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games
    Yulai Zhao, Yuandong Tian, Jason D. Lee, Simon S. Du
    International Conference on Artificial Intelligence and Statistics (AISTATS) 2022
    [paper] [arXiv]