Statistical Learning
Universality laws for Gaussian mixtures in generalized linear models
Gaussian mixtures are a popular model in high-dimensional statistics since, besides being an universal approximator, they often lead to mathematically tractable problems.
Sparse Deep Learning for Time Series Data: Theory and Applications
Specifically, we lay the foundation within the Bayesian framework.
Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization Jun Chen
We establish the first generalization error bound of FedZO under the Lipschitz continuity and smoothness conditions.
Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing
Finding an approximate second-order stationary point (SOSP) is a well-studied and fundamental problem in stochastic nonconvex optimization with many applications in machine learning.
