research
∙
05/26/2023
Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models
Wasserstein distributionally robust estimators have emerged as powerful ...
share
research
∙
11/15/2022
On the rate of convergence of Bregman proximal methods in constrained variational inequalities
We examine the last-iterate convergence rate of Bregman proximal methods...
research
∙
09/03/2022
Entropy-regularized Wasserstein distributionally robust shape and topology optimization
This brief note aims to introduce the recent paradigm of distributional ...
research
∙
06/08/2022
Push–Pull with Device Sampling
We consider decentralized optimization problems in which a number of age...
research
∙
02/26/2022
Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)
This paper is an extended version of [Burashnikova et al., 2021, arXiv: ...
research
∙
07/05/2021
The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities
In this paper, we analyze the local convergence rate of optimistic mirro...
research
∙
05/27/2021
Optimization in Open Networks via Dual Averaging
In networks of autonomous agents (e.g., fleets of vehicles, scattered se...
research
∙
12/21/2020
Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism
Online learning has been successfully applied to many problems in which ...
research
∙
10/02/2020
Nonsmoothness in Machine Learning: specific structure, proximal identification, and applications
Nonsmoothness is often a curse for optimization; but it is sometimes a b...
research
∙
09/25/2020
Randomized Progressive Hedging methods for Multi-stage Stochastic Programming
Progressive Hedging is a popular decomposition algorithm for solving mul...
research
∙
09/01/2020
Rank-one partitioning: formalization, illustrative examples, and a new cluster enhancing strategy
In this paper, we introduce and formalize a rank-one partitioning learni...
research
∙
03/23/2020
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling
Owing to their stability and convergence speed, extragradient methods ha...
research
∙
08/22/2019
On the convergence of single-call stochastic extra-gradient methods
Variational inequalities have recently attracted considerable interest i...
research
∙
12/10/2018
Asynchronous Distributed Learning with Sparse Communications and Identification
In this paper, we present an asynchronous optimization algorithm for dis...
research
∙
06/25/2018
A Distributed Flexible Delay-tolerant Proximal Gradient Algorithm
We develop and analyze an asynchronous algorithm for distributed convex ...
research
∙
01/17/2018
On the Proximal Gradient Algorithm with Alternated Inertia
In this paper, we investigate the attractive properties of the proximal ...
research
∙
05/22/2017
An Asynchronous Distributed Framework for Large-scale Learning Based on Parameter Exchanges
In many distributed learning problems, the heterogeneous loading of comp...
research
∙
01/23/2017