Professor of Statistics and Data Science Yale University 10 Hillhouse Ave Room 235 New Haven, CT 06511
📧 yihong.wu@yale.edu ✉ 24 Hillhouse Ave ☏ 203-436-9003
I am broadly interested in the theoretical and algorithmic aspects of high-dimensional statistics, information theory, and optimization.
Andre Wibisono, Yihong Wu, Kaylee Yingxi Yang, "Optimal score estimation via empirical Bayes smoothing", Feb 2024.
Leon Lufkin, Yihong Wu, Jiaming Xu, "Sharp Information-Theoretic Thresholds for Shuffled Linear Regression", Feb 2024.
Zhou Fan, Leying Guan, Yandi Shen, Yihong Wu, "Gradient flows for empirical Bayes in high-dimensional linear models", Dec 2023.
Soham Jana, Yury Polyanskiy, Anzo Teh, and Yihong Wu, "Empirical Bayes via ERM and Rademacher complexities: the Poisson model", Conference on Learning Theory (COLT), 2023.
Zeyu Jia, Yury Polyanskiy, and Yihong Wu, "Entropic characterization of optimal rates for learning Gaussian mixtures", Conference on Learning Theory (COLT), 2023.
Yun Ma, Yihong Wu, and Pengkun Yang, "On the best approximation by finite Gaussian mixtures", 2023 IEEE International Symposium on Information Theory (ISIT), 2023, pp. 2619–2624.
Yutong Nie and Yihong Wu, "Large-scale multiple testing: Fundamental limits of false discovery rate control and compound oracle?", Feb 2023.
Yandi Shen and Yihong Wu, "Empirical Bayes estimation: When does -modeling beat -modeling in theory (and in practice)?", Nov 2022.