30 citations · 148 across the 30 of their papers we have counts for
7 papers · 1 filter
Generalization Bounds for Inductive Matrix Completion in Low-noise Settings
Antoine Ledent, Rodrigo Alves, Yunwen Lei +2
We study inductive matrix completion (matrix completion with side information) under an i.i.d. subgaussian noise assumption at a low noise regime, with uniform sampling of the entr…
On Stability and Generalization of Bilevel Optimization Problem
Meng Ding, Mingxi Lei, Yunwen Lei +2
(Stochastic) bilevel optimization is a frequently encountered problem in machine learning with a wide range of applications such as meta-learning, hyper-parameter optimization, and…
Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks
Yunwen Lei, Rong Jin, Yiming Ying
While significant theoretical progress has been achieved, unveiling the generalization mystery of overparameterized neural networks still remains largely elusive. In this paper, we…
Stability and Generalization for Markov Chain Stochastic Gradient Methods
Puyu Wang, Yunwen Lei, Yiming Ying +1
Recently there is a large amount of work devoted to the study of Markov chain stochastic gradient methods (MC-SGMs) which mainly focus on their convergence analysis for solving min…
Differentially Private Stochastic Gradient Descent with Low-Noise
Puyu Wang, Yunwen Lei, Yiming Ying +1
Modern machine learning algorithms aim to extract fine-grained information from data to provide accurate predictions, which often conflicts with the goal of privacy protection. Thi…
Stability and Generalization of Stochastic Optimization with Nonconvex and Nonsmooth Problems
Yunwen Lei
Stochastic optimization has found wide applications in minimizing objective functions in machine learning, which motivates a lot of theoretical studies to understand its practical…
Differentially Private SGDA for Minimax Problems
Zhenhuan Yang, Shu Hu, Yunwen Lei +3
Stochastic gradient descent ascent (SGDA) and its variants have been the workhorse for solving minimax problems. However, in contrast to the well-studied stochastic gradient descen…