Xuechen Li

Research

I completed my PhD in early 2024 and joined industry. Before that, I was a PhD student at Stanford CS where I was affiliated with the Stanford Artificial Intelligence Laboratory (SAIL), the Stanford Machine Learning Group, and the Stanford NLP Group.

I was fortunate to be advised by Tatsunori Hashimoto and Carlos Guestrin. My research was supported by a Stanford Graduate Fellowship and a Meta PhD Fellowship in Security and Privacy while I was a PhD student.

Selected Research Articles (full list see google scholar)

• AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
  Yann Dubois*, Xuechen Li*, Rohan Taori*, Tianyi Zhang*, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto
  Advances in Neural Information Processing Systems, 2023 [Spotlight]
  [paper] [code] [blog]
• Alpaca: A Strong, Replicable Instruction-Following Model
  Rohan Taori*, Ishaan Gulrajani*, Tianyi Zhang*, Yann Dubois*, Xuechen Li*, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto
  [code] [blog]
• Foundation Models and Fair Use
  Peter Henderson*, Xuechen Li*, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley, Percy Liang
  Journal of Machine Learning Research
  
• Large Language Models Can Be Strong Differentially Private Learners
  Xuechen Li, Florian Tramer, Percy Liang, Tatsunori Hashimoto
  International Conference on Learning Representations, 2022 [Oral]
  NeurIPS Privacy in Machine Learning Workshop, 2021 [Oral]
  [code] [slides]
• When Does Preconditioning Help or Hurt Generalization?
  Shun-ichi Amari, Jimmy Ba, Roger Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu
  International Conference on Learning Representations, 2021
  12th OPT Workshop on Optimization for ML [Best student paper]
  
• Scalable Gradients for Stochastic Differential Equations
  Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud
  International Conference on Artificial Intelligence and Statistics, 2020
  2nd Symposium on Advances in Approximate Bayesian Inference [Spotlight]
  [code] [slides]
• Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond
  Xuechen Li, Denny Wu, Lester Mackey, Murat A. Erdogdu
  Advances in Neural Information Processing Systems, 2019 [Spotlight]