Zhou Fan

most citedGradient flows for empirical Bayes in high-dimensional linear models

2 citations · 2 across the 4 of their papers we have counts for

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math.ST2026

Empirical Bayes linear regression in high dimensions: Method of moments and sub-linear sample complexity

Zhou Fan, Yandi Shen, Haoyu Wang +1

We study empirical Bayes estimation of the prior in high-dimensional linear regression $y = X β + ε$ , where the regression coefficients are d…

math.ST2026

Geometric planted matchings in high dimensions: The power of multiple views

Timothy L. H. Wee, Kaylee Y. Yang, Zhou Fan +1

We study the problem of recovering the correspondence between a collection of $n$ points in $R^{d}$ and a noisy, permuted version of those points. In the high-dimensional re…

math.ST2026

Bayesian inference of planted matchings: Local posterior approximation and infinite-volume limit

Zhou Fan, Timothy L. H. Wee, Kaylee Y. Yang

We study Bayesian inference of an unknown matching $π^{*}$ between two correlated random point sets ${X_{i}}_{i = 1}^{n}$ and ${Y_{i}}_{i = 1}^{n}$ in $[0, 1]^{d}$ , under a critical scaling $\|…

math.ST2023★ 2 cited

Gradient flows for empirical Bayes in high-dimensional linear models

Zhou Fan, Leying Guan, Yandi Shen +1

Empirical Bayes provides a powerful framework for learning and adapting to latent structure in data. In sequence models where an independent observation is associated to each laten…