4 citations · 7 across the 12 of their papers we have counts for
13 papers
Dynamical mean-field limit and replica-symmetric free energy for the orthogonally-invariant SK model
Zhou Fan, Theodor Misiakiewicz, Leda Wang +1
We study a class of diffusion processes on interacting through a symmetric matrix . When eigenvectors of are Haar-uniform on the orth…
Anisotropic local law for non-separable sample covariance matrices
Zhou Fan, Renyuan Ma, Elliot Paquette +1
We establish local laws for sample covariance matrices $K = N^{-1}\sum_{i=1}^N \g_i\g_i^*$ where the random vectors $\g_1, \ldots, \g_N \in \R^n$ are independent with common covari…
High-dimensional learning dynamics of multi-pass Stochastic Gradient Descent in multi-index models
Zhou Fan, Leda Wang
The learning dynamics of Stochastic Gradient Descent (SGD) are believed to play an important role in influencing the generalization properties of trained models. While the precise…
When does Gaussian equivalence fail and how to fix it: Non-universal behavior of random features with quadratic scaling
Garrett G. Wen, Hong Hu, Yue M. Lu +2
A major effort in modern high-dimensional statistics has been devoted to the analysis of linear predictors trained on nonlinear feature embeddings via empirical risk minimization (…
ASearch: Ambiguity-Aware Question Answering with Reinforcement Learning
Fengji Zhang, Xinyao Niu, Chengyang Ying +7
Recent advances in Large Language Models (LLMs) and Reinforcement Learning (RL) have led to strong performance in open-domain question answering (QA). However, existing models stil…
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…
Kronecker-product random matrices and a matrix least squares problem
Zhou Fan, Renyuan Ma
We study the eigenvalue distribution and resolvent of a Kronecker-product random matrix model $A \otimes I_{n \times n}+I_{n \times n} \otimes B+Θ\otimes Ξ\in \mathbb{C}^{n^2 \time…
Asymptotic mutual information in quadratic estimation problems over compact groups
Kaylee Y. Yang, Timothy L. H. Wee, Zhou Fan
Motivated by applications to group synchronization and quadratic assignment on random data, we study a general problem of Bayesian inference of an unknown ``signal'' belonging to a…
Nonlinear spiked covariance matrices and signal propagation in deep neural networks
Zhichao Wang, Denny Wu, Zhou Fan
Many recent works have studied the eigenvalue spectrum of the Conjugate Kernel (CK) defined by the nonlinear feature map of a feedforward neural network. However, existing results…
Optimal Automated Market Makers: Differentiable Economics and Strong Duality
Michael J. Curry, Zhou Fan, David C. Parkes
The role of a market maker is to simultaneously offer to buy and sell quantities of goods, often a financial asset such as a share, at specified prices. An automated market maker (…
Mean-field variational inference with the TAP free energy: Geometric and statistical properties in linear models
Michael Celentano, Zhou Fan, Licong Lin +1
We study mean-field variational inference in a Bayesian linear model when the sample size n is comparable to the dimension p. In high dimensions, the common approach of minimizing…
Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces
Zhou Fan, Xinran Han, Zi Wang
Bayesian optimization (BO) is a popular black-box function optimization method, which makes sequential decisions based on a Bayesian model, typically a Gaussian process (GP), of th…