Optimal empirical Bayes estimation for the Poisson model via minimum-distance methods
Accepted, Information and Inference, 2025
with Yury Polyanskiy and Yihong Wu [Paper link]
Publications
(Authors lists usually in alphabetical order of surnames. Exceptions denoted with “*”.)
A provable initialization and robust clustering method for general mixture models
IEEE Transactions on Information Theory, 2025
with Jianqing Fan and Sanjeev Kulkarni [Paper link]
Multilook Coherent Imaging: Theoretical Guarantees and Algorithms
Preprint, 2025
with Xi Chen, Christopher A Metzler, Arian Maleki, and Shirin Jalali [Paper link]
Factor Informed Double Deep Learning For Average Treatment Effect Estimation
Preprint, 2025
with Jianqing Fan, Sanjeev Kulkarni, and Qishuo Yin [Paper link]
Factor Adjusted Spectral Clustering for Mixture Models
Preprint, 2024
with Shange Tang and Jianqing Fan [Paper link]
Support recovery with stochastic gates: theory and application for linear Models
Elsevier Singal Processing, 2023
with Henry Li, Yutaro Yamada and Ofir Lindenbaum [Paper link]
Adversarially robust clustering with optimality guarantees
Preprint, 2023
with Kun Yang and Sanjeev Kulkarni [Paper link]
Optimal prediction of Markov chains with and without spectral gap
NeurIPS, 2021. Longer version in IEEE Transactions on Information Theory, 2023
with Yanjun Han and Yihong Wu [Paper link]
Empirical Bayes via ERM and Rademacher complexities: the Poisson model
Conference on Learning Theory, 2023
with Anzo Teh, Yury Polyanskiy and Yihong Wu [Paper link]
Extrapolating the profile of a finite population
Conference on Learning Theory, 2020
with Yury Polyanskiy and Yihong Wu [Paper link]
A characterization of all single-integral, non-kernel divergence estimators.*
IEEE Transactions on Information Theory, 2019
with Ayanendranath Basu [Paper link]