Current techniques for deep neural network (DNN) pruning often involve intricate multi-step
processes that require domain-specific expertise making their widespread adoption …

The minimax problems arise throughout machine learning applications, ranging from
adversarial training and policy evaluation in reinforcement learning to AUROC …

In this paper, we study the nonconvex-strongly-concave minimax optimization problem on
decentralized setting. The minimax problems are attracting increasing attentions because of …

We propose a new stochastic method SAPD+ for solving nonconvex-concave minimax
problems of the form $\min\max\mathcal {L}(x, y)= f (x)+\Phi (x, y)-g (y) $, where $ f, g $ are …

Adaptive algorithms like AdaGrad and AMSGrad are successful in nonconvex optimization
owing to their parameter-agnostic ability–requiring no a priori knowledge about problem …

The minimax optimization over Riemannian manifolds (possibly nonconvex constraints) has
been actively applied to solve many problems, such as robust dimensionality reduction and …

We provide a unified analysis of two-timescale gradient descent ascent (TTGDA) for solving
structured nonconvex minimax optimization problems in the form of $\min_x\max_ {y\in Y} f …

This paper is concerned with a novel deep learning method for variational problems with
essential boundary conditions. To this end, we first reformulate the original problem into a …

This paper focuses on stochastic methods for solving smooth non-convex strongly-concave
min-max problems, which have received increasing attention due to their potential …

In the paper, we study a class of useful minimax problems on Riemanian manifolds and
propose a class of effective Riemanian gradient-based methods to solve these minimax …