Recently, significant progress has been made in understanding the
genera...
Recently, contrastive learning has found impressive success in advancing...
We study inductive matrix completion (matrix completion with side
inform...
(Stochastic) bilevel optimization is a frequently encountered problem in...
Recently there is a large amount of work devoted to the study of Markov ...
In this paper, by introducing a low-noise condition, we study privacy an...
Stochastic optimization has found wide applications in minimizing object...
Stochastic gradient descent ascent (SGDA) and its variants have been the...
Pairwise learning refers to learning tasks where the loss function depen...
We propose a novel training methodology – Concept Group Learning (CGL) –...
Randomized coordinate descent (RCD) is a popular optimization algorithm ...
In machine learning we often encounter structured output prediction prob...
Many machine learning problems can be formulated as minimax problems suc...
Many fundamental machine learning tasks can be formulated as a problem o...
In this paper, we are concerned with differentially private SGD algorith...
In this paper, we aim to develop stochastic hard thresholding algorithms...
Recently there are a considerable amount of work devoted to the study of...
Over the last decade, research on automated parameter tuning, often refe...
In this paper we consider the problem of maximizing the Area under the R...
Using proof techniques involving L^∞ covering numbers, we show
generalis...
Domain generalization is the problem of assigning labels to an unlabeled...
Stochastic gradient descent (SGD) is a popular and efficient method with...
In this paper we consider online mirror descent (OMD) algorithms, a clas...
This paper provides a general result on controlling local Rademacher
com...