Yuxin Chen
Openings
I'm looking for highly motivated postdocs and Ph. D. students with strong mathematical background and interest in machine learning theory (particularly diffusion models, and LLM), statistics, and optimization.
Recent news
|
- We will teach a tutorial at ISIT 2024 on reinforcement learning theory (together with Y. Chi and Y. Wei).
- Yuchen Zhou will join UIUC
as an Assistant Professor in Fall 2024. Congrats, Yuchen!
- Yuling Yan received the IMS Lawrence D. Brown Ph. D. Student Award and the ICCM Best Thesis Award (silver medal). Congrats, Yuling!
- Changxiao Cai has joined University of Michigan-Ann Arbor as an Assistant Professor. Congrats, Changxiao!
- Gen Li has joined Chinese University of Hong Kong as an Assistant Professor. Congrats, Gen!
- Yuling Yan will join University of Wisconsin-Madison as an Assistant Professor in Fall 2024, after being a Norbert Wiener postdoc at MIT (advised by P. Rigollet and M. Wainwright). Congrats, Yuling!
- Qian Yu has joined UCSB as an Assistant Professor. Congrats, Qian!
|
Topic courses I have developed
Selected recent papers
Machine learning theory
G. Li*, Y. Huang*, T. Efimov, Y. Wei, Y. Chi, Y. Chen, “Accelerating convergence of score-based diffusion models, provably,” 2024 (*=equal contributions).
Z. Zhang, W. Zhan, Y. Chen, S. S. Du, J. D. Lee, “Optimal multi-distribution learning,” 2023. [slides]
Z. Zhang, Y. Chen, J. D. Lee, S. S. Du, “Settling the sample complexity of online reinforcement learning,” 2023. [slides]
G. Li, Y. Wei, Y. Chen, Y. Chi, “Towards faster non-asymptotic convergence for diffusion-based generative models,” 2023 (accepted in part to ICLR 2024).
G. Li, Y. Yan, Y. Chen, J. Fan, “Minimax-optimal reward-agnostic exploration in reinforcement learning,” 2023.
G. Li, L. Shi, Y. Chen, Y. Chi, Y. Wei, “Settling the sample complexity of model-based offline reinforcement learning,” Annals of Statistics, vol. 52, no. 1, pp. 233-260, 2024. [paper][AoS version]
G. Li, Y. Chi, Y. Wei, Y. Chen, “Minimax-optimal multi-agent RL in Markov games with a generative model,” NeurIPS 2022 (selected as oral).
G. Li, Y. Wei, Y. Chi, Y. Chen, “Softmax policy gradient methods can take exponential time to converge,” Mathematical Programming, vol. 201, pp. 707-802, 2023 (appeared in part to COLT 2021). [paper][MP version][slides]
G. Li, C. Cai, Y. Chen, Y. Wei, Y. Chi, “Is Q-Learning minimax optimal? A tight sample complexity analysis,” Operations Research, vol. 72, no. 1, pp. 203-221, 2024 (appeared in part to ICML 2021). [paper][OR version]
S. Cen, C. Cheng, Y. Chen, Y. Wei, Y. Chi, “Fast global convergence of natural policy gradient methods with entropy regularization,” Operations Research, vol. 70, no. 4, pp. 2563–2578, 2022 (INFORMS George Nicholson award finalist, 2021). [ArXiv][OR version][slides]
G. Li, Y. Wei, Y. Chi, Y. Chen, “Breaking the sample size barrier in model-based reinforcement learning with a generative model,” Operations Research, vol. 72, no. 1, pp. 222-236, 2024 (appeared in part to NeurIPS 2020). [paper][OR version][slides]
Spectral methods
Y. Zhou, Y. Chen, “Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA,” 2023. [ArXiv][slides]
Y. Yan, Y. Chen, J. Fan, “Inference for heteroskedastic PCA with missing data,” accepted to Annals of Statistics, 2024+. [ArXiv][slides]
C. Cai, G. Li, Y. Chi, H. V. Poor, Y. Chen, “Subspace estimation from unbalanced and incomplete data matrices, $\ell_{2,\infty}$ statistical guarantees ” Annals of Statistics, vol. 49, no. 2, pp. 944-967, 2021. [ArXiv][AoS version]
Y. Chen, C. Cheng, J. Fan, “Asymmetry helps: Eigenvalue and eigenvector analyses of asymmetrically perturbed low-rank matrices,” Annals of Statistics, vol. 49, no. 1, pp. 435-458, 2021. [ArXiv][AoS version][slides]
Y. Chen, J. Fan, C. Ma, K. Wang, “Spectral method and regularized MLE are both optimal for top-K ranking,” Annals of Statistics, vol. 47, no. 4, pp. 2204-2235, August 2019. [ArXiv][AoS version][slides]
|