Recently there are a considerable amount of work devoted to the study of the algorithmic
stability and generalization for stochastic gradient descent (SGD). However, the existing …

Stochastic gradient descent (SGD) is a popular and efficient method with wide applications
in training deep neural nets and other nonconvex models. While the behavior of SGD is well …

We study online composite optimization under the Stochastically Extended Adversarial
(SEA) model. Specifically, each loss function consists of two parts: a fixed non-smooth and …

We investigate the convergence of stochastic mirror descent (SMD) under interpolation in
relatively smooth and smooth convex optimization. In relatively smooth convex optimization …

Stochastic gradient descent (SGD) is the workhorse in modern machine learning and data-
driven optimization. Despite its popularity, existing theoretical guarantees for SGD are …

Stochastic gradient descent (SGD) has become the method of choice for training highly
complex and nonconvex models since it can not only recover good solutions to minimize …

Stochastic gradient descent (SGD) has become the method of choice to tackle large-scale
datasets due to its low computational cost and good practical performance. Learning rate …

Improving sample efficiency has been a longstanding goal in reinforcement learning. This
paper proposes VRMPO algorithm: a sample efficient policy gradient method with stochastic …

This article considers a game-theoretic framework for distributed empirical risk minimization
(ERM) problems over networks where the information acquisition at a node is modeled as a …

This article investigates the estimation and generalization errors of the generative
adversarial network (GAN) training. On the statistical side, we develop an upper bound as …