Yuling Yan

Starting Aug 2024, I am an assistant professor in the Department of Statistics at University of Wisconsin-Madison. Previously, I spent one year as a Norbert Wiener Postdoctoral Associate at MIT, working with Professor Philippe Rigollet and Professor Martin Wainwright. I obtained my Ph.D. degree at Princeton University in 2023, advised by Professor Yuxin Chen and Professor Jianqing Fan. I received my bachelor's degree from Peking University in 2018. Research interests: statistics, mathematics of data science, and their applications to generative AI and social sciences. Contact: 7277 Medical Science Center 1300 University Ave, Madison, WI 53706 E-mail: yuling.yan [@] wisc [DOT] edu

Featured projects

(*=equal contribution, -=alphabetical order)

• Causal inference under staggered adoption

  • Y. Yan, M. J. Wainwright, “Entrywise Inference for Causal Panel Data: A Simple and Instance-Optimal Approach,” 2024

  • E. Xia*, Y. Yan*, M. J. Wainwright, “Inference under Staggered Adoption: Case Study of the Affordable Care Act,” available upon request.
    

• Learning in the space of probability measures (with applications to generative AI and nonparametric statistics)

  • Y. Yan*, K. Wang*, P. Rigollet, “Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow,” Annals of Statistics, vol. 52, no. 4, pp. 1774-1795, 2024

  • G. Li*, Y. Yan*, “Adapting to Unknown Low-Dimensional Structures in Score-Based Diffusion Models,” NeurIPS 2024

  • G. Li*, Y. Yan*, “A Score-Based Density Formula, with Applications in Diffusion Generative Models,” 2024

  • G. Li*, Y. Yan*, “O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions,” 2024

• Mechanism design for improving peer review

  • Y. Yan, W. J. Su, J. Fan, “Isotonic Mechanism for Exponential Family Estimation in Machine Learning Peer Review,” under major revision at Journal of the Royal Statistical Society: Series B, 2024

  • B. Su, J. Zhang, N. Collina, Y. Yan, D. Li, K. Cho, J. Fan, A. Roth, W. J. Su “Analysis of the ICML 2023 Ranking Data: Can Authors’ Opinions of Their Own Papers Assist Peer Review in Machine Learning?” 2024

• Inference and uncertainty quantification for low-rank models

  • Y. Chen, J. Fan, C. Ma, Y. Yan (-), “Inference and Uncertainty Quantification for Noisy Matrix Completion,” Proceedings of the National Academy of Sciences, vol. 116, no. 46, pp. 22931-22937, 2019

  • Y. Yan, Y. Chen, J. Fan, “Inference for Heteroskedastic PCA with Missing Data,” Annals of Statistics, vol. 52, no. 2, pp. 729–756, 2024

  • J. Fan, Y. Yan, Y. Zheng (-) “When Can Weak Latent Factors Be Statistically Inferred?” 2024

• Bridging convex and nonconvex optimization in low-rank estimation

  • Y. Chen, C. Chi, J. Fan, C. Ma, Y. Yan (-), “Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization,” SIAM Journal on Optimization, vol. 30, no. 4, pp. 3098-3121, 2020

  • Y. Chen, J. Fan, C. Ma, Y. Yan (-), “Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data,” Annals of Statistics, vol. 49, no. 5, pp. 2948-2971, 2021

  • Y. Chen, J. Fan, B. Wang, Y. Yan (-), “Convex and Nonconvex Optimization Are Both Minimax-Optimal for Noisy Blind Deconvolution Under Random Designs,” Journal of the American Statistical Association, vol. 118, no. 542, pp. 858-868, 2023

• Statistical foundations of offline reinforcement learning

  • Y. Yan, G. Li, Y. Chen, J. Fan, “The Efficacy of Pessimism in Asynchronous Q-learning,” IEEE Transactions on Information Theory, vol. 69, no. 11, pp. 7185-7219, 2023

  • Y. Yan, G. Li, Y. Chen, J. Fan, “Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games,” accepted to Operations Research, 2024

  • G. Li, Y. Yan, Y. Chen, J. Fan, “Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning,” 2024 (accepted in part to COLT 2024)

Selected awards

• IMS Lawrence D. Brown Award, 2024

• ICCM Best Thesis Award (Silver Medal), 2024

• Norbert Wiener Postdoctoral Fellowship, MIT, 2023

• Charlotte Elizabeth Procter Honorific Fellowship, Princeton University, 2022

• ASA Statistical Learning and Data Science Best Student Paper Award, 2022