Yihong Wu

Research interests

I am broadly interested in the theoretical and algorithmic aspects of high-dimensional statistics, information theory, and optimization.

Recent papers

• Jiafeng Chen and Yihong Wu, "Sharp regret-Hellinger bounds for Gaussian empirical Bayes via polynomial approximation", May 2026.

• Cheng Mao, Yihong Wu, and Jiaming Xu, "Random geometric graphs with smooth kernels: sharp detection threshold and a spectral conjecture", Feb 2026.

• Yanjun Han, Jonathan Niles-Weed, Yandi Shen, and Yihong Wu, "Besting Good–Turing: Optimality of Non-Parametric Maximum Likelihood for Distribution Estimation", Sep 2025.

• Simiao Jiao, Yihong Wu, and Jiaming Xu, "The broken sample problem revisited: Proof of a conjecture by Bai-Hsing and high-dimensional extensions", Mar 2025.

• Zhou Fan, Leying Guan, Yandi Shen, and Yihong Wu, "Gradient flows for empirical Bayes in high-dimensional linear models", Dec 2023.

• Yutong Nie and Yihong Wu, "Large-scale multiple testing: Fundamental limits of false discovery rate control and compound oracle?", The Annals of Statistics, 2026.

• Yandi Shen and Yihong Wu, "Poisson Empirical Bayes estimation: When does -modeling beat -modeling in theory (and in practice)?", The Annals of Statistics, 2026.