Sharpness-aware minimization (SAM) has well documented merits in enhancing
generalization of deep neural networks, even without sizable data augmentation. Embracing …

Submodular maximization under various constraints is a fundamental problem studied
continuously, in both computer science and operations research, since the late 1970's. A …

We analyze the sample complexity of single-loop quadratic penalty and augmented
Lagrangian algorithms for solving nonconvex optimization problems with functional equality …

This paper presents a unified approach for maximizing continuous DR-submodular functions
that encompasses a range of settings and oracle access types. Our approach includes a …

Abstract The Conditional Gradient (or Frank-Wolfe) method is one of the most well-known
methods for solving constrained optimization problems appearing in various machine …

We consider stochastic optimization problems with functional constraints. If the objective and
constraint functions are not convex, the classical stochastic approximation algorithms such …

This paper introduces unified projection-free Frank-Wolfe type algorithms for adversarial
continuous DR-submodular optimization, spanning scenarios such as full information and …

Projected Gradient Ascent (PGA) is the most commonly used optimization scheme in
machine learning and operations research areas. Nevertheless, numerous studies and …

In this paper, we study online diminishing return submodular (DR-submodular for short)
maximization in the bandit setting. Our focus is on problems where the reward functions can …

In this paper, we propose DP-FedFW, a novel Frank-Wolfe based federated learning
algorithm with local (ϵ, δ)-differential privacy (DP) guarantees in a constrained learning …