2 citations · 2 across the 7 of their papers we have counts for
7 papers
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 , where the regression coefficients are d…
Learning single index model with gradient descent: spectral initialization and precise asymptotics
Yuchen Chen, Yandi Shen
Non-convex optimization plays a central role in many statistics and machine learning problems. Despite the landscape irregularities for general non-convex functions, some recent wo…
Besting Good--Turing: Optimality of Non-Parametric Maximum Likelihood for Distribution Estimation
Yanjun Han, Jonathan Niles-Weed, Yandi Shen +1
When faced with a small sample from a large universe of possible outcomes, scientists often turn to the venerable Good--Turing estimator. Despite its pedigree, however, this estima…
Dynamical mean-field analysis of adaptive Langevin diffusions: Replica-symmetric fixed point and empirical Bayes
Zhou Fan, Justin Ko, Bruno Loureiro +2
In many applications of statistical estimation via sampling, one may wish to sample from a high-dimensional target distribution that is adaptively evolving to the samples already s…
Dynamical mean-field analysis of adaptive Langevin diffusions: Propagation-of-chaos and convergence of the linear response
Zhou Fan, Justin Ko, Bruno Loureiro +2
Motivated by an application to empirical Bayes learning in high-dimensional regression, we study a class of Langevin diffusions in a system with random disorder, where the drift co…
Triangle Steepest Descent: A Geometry-Based Gradient Algorithm with Guaranteed R-Linear Convergence
Ya Shen, Qing-Na Li, Yu-Hong Dai
Gradient methods are among the simplest yet most widely used algorithms for unconstrained optimization. Motivated by a geometric property of the steepest descent (SD) method that c…
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…