Panayotis Mertikopoulos

research

∙ 02/05/2023

Learning in quantum games

In this paper, we introduce a class of learning dynamics for general qua...

0 Kyriakos Lotidis, et al. ∙

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...

0 Waïss Azizian, et al. ∙

research

∙ 10/23/2022

Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee

We propose and analyze exact and inexact regularized Newton-type methods...

0 Tianyi Lin, et al. ∙

research

∙ 10/17/2022

On the convergence of policy gradient methods to Nash equilibria in general stochastic games

Learning in stochastic games is a notoriously difficult problem because,...

0 Angeliki Giannou, et al. ∙

research

∙ 09/17/2022

Survival of dominated strategies under imitation dynamics

The literature on evolutionary game theory suggests that pure strategies...

0 Panayotis Mertikopoulos, et al. ∙

research

∙ 09/11/2022

Learning in Games with Quantized Payoff Observations

This paper investigates the impact of feedback quantization on multi-age...

0 Kyriakos Lotidis, et al. ∙

research

∙ 07/15/2022

Pick your Neighbor: Local Gauss-Southwell Rule for Fast Asynchronous Decentralized Optimization

In decentralized optimization environments, each agent i in a network of...

6 Marina Costantini, et al. ∙

research

∙ 06/19/2022

Nested bandits

In many online decision processes, the optimizing agent is called to cho...

0 Matthieu Martin, et al. ∙

research

∙ 06/14/2022

The Dynamics of Riemannian Robbins-Monro Algorithms

Many important learning algorithms, such as stochastic gradient methods,...

0 Mohammad Reza Karimi, et al. ∙

research

∙ 06/13/2022

No-Regret Learning in Games with Noisy Feedback: Faster Rates and Adaptivity via Learning Rate Separation

We examine the problem of regret minimization when the learner is involv...

0 Yu-Guan Hsieh, et al. ∙

research

∙ 06/08/2022

Learning in games from a stochastic approximation viewpoint

We develop a unified stochastic approximation framework for analyzing th...

0 Panayotis Mertikopoulos, et al. ∙

research

∙ 01/09/2022

Routing in an Uncertain World: Adaptivity, Efficiency, and Equilibrium

We consider the traffic assignment problem in nonatomic routing games wh...

0 Dong Quan Vu, et al. ∙

research

∙ 09/13/2021

Zeroth-order non-convex learning via hierarchical dual averaging

We propose a hierarchical version of dual averaging for zeroth-order onl...

0 Amélie Héliou, et al. ∙

research

∙ 08/10/2021

A heuristic for estimating Nash equilibria in first-price auctions with correlated values

Our paper concerns the computation of Nash equilibria of first-price auc...

0 Benjamin Heymann, et al. ∙

research

∙ 07/16/2021

Adaptive first-order methods revisited: Convex optimization without Lipschitz requirements

We propose a new family of adaptive first-order methods for a class of c...

0 Kimon Antonakopoulos, et al. ∙

research

∙ 07/06/2021

Distributed stochastic optimization with large delays

One of the most widely used methods for solving large-scale stochastic o...

12 Zhengyuan Zhou, et al. ∙

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...

0 Waïss Azizian, et al. ∙

research

∙ 07/04/2021

Learning in nonatomic games, Part I: Finite action spaces and population games

We examine the long-run behavior of a wide range of dynamics for learnin...

0 Saeed Hadikhanloo, et al. ∙

research

∙ 06/28/2021

Asymptotic Degradation of Linear Regression Estimates With Strategic Data Sources

We consider the problem of linear regression from strategic data sources...

0 Nicolas Gast, et al. ∙

research

∙ 05/27/2021

Optimization in Open Networks via Dual Averaging

In networks of autonomous agents (e.g., fleets of vehicles, scattered se...

0 Yu-Guan Hsieh, et al. ∙

research

∙ 04/26/2021

Adaptive Learning in Continuous Games: Optimal Regret Bounds and Convergence to Nash Equilibrium

In game-theoretic learning, several agents are simultaneously following ...

0 Yu-Guan Hsieh, et al. ∙

research

∙ 01/12/2021

Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information

In this paper, we examine the Nash equilibrium convergence properties of...

0 Angeliki Giannou, et al. ∙

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 ...

0 Yu-Guan Hsieh, et al. ∙

research

∙ 10/22/2020

Adaptive extra-gradient methods for min-max optimization and games

We present a new family of min-max optimization algorithms that automati...

0 Kimon Antonakopoulos, et al. ∙

research

∙ 10/19/2020

No-regret learning and mixed Nash equilibria: They do not mix

Understanding the behavior of no-regret dynamics in general N-player gam...

0 Lampros Flokas, et al. ∙

research

∙ 10/16/2020

Online non-convex optimization with imperfect feedback

We consider the problem of online learning with non-convex losses. In te...

0 Amélie Héliou, et al. ∙

research

∙ 10/13/2020

Regret minimization in stochastic non-convex learning via a proximal-gradient approach

Motivated by applications in machine learning and operations research, w...

0 Nadav Hallak, et al. ∙

research

∙ 06/19/2020

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems

This paper analyzes the trajectories of stochastic gradient descent (SGD...

0 Panayotis Mertikopoulos, et al. ∙

research

∙ 06/19/2020

Gradient-free Online Learning in Games with Delayed Rewards

Motivated by applications to online advertising and recommender systems,...

0 Amélie Héliou, et al. ∙

research

∙ 06/16/2020

The limits of min-max optimization algorithms: convergence to spurious non-critical sets

Compared to minimization problems, the min-max landscape in machine lear...

0 Ya-Ping Hsieh, et al. ∙

research

∙ 06/09/2020

Fast Gradient-Free Optimization in Distributed Multi-User MIMO Systems

In this paper, we develop a gradient-free optimization methodology for e...

0 Olivier Bilenne, et al. ∙

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...

0 Yu-Guan Hsieh, et al. ∙

research

∙ 03/21/2020

A new regret analysis for Adam-type algorithms

In this paper, we focus on a theory-practice gap for Adam and its varian...

0 Ahmet Alacaoglu, et al. ∙

research

∙ 02/23/2020

Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games

We consider multi-agent learning via online gradient descent (OGD) in a ...

7 Tianyi Lin, et al. ∙

research

∙ 01/02/2020

Quick or cheap? Breaking points in dynamic markets

We examine two-sided markets where players arrive stochastically over ti...

0 Panayotis Mertikopoulos, et al. ∙

research

∙ 08/22/2019

On the convergence of single-call stochastic extra-gradient methods

Variational inequalities have recently attracted considerable interest i...

0 Yu-Guan Hsieh, et al. ∙

research

∙ 02/09/2019

Forward-backward-forward methods with variance reduction for stochastic variational inequalities

We develop a new stochastic algorithm with variance reduction for solvin...

0 Radu Ioan Bot, et al. ∙

research

∙ 10/03/2018

Bandit learning in concave N-person games

This paper examines the long-run behavior of learning with bandit feedba...

0 Mario Bravo, et al. ∙

research

∙ 09/25/2018

Hessian barrier algorithms for linearly constrained optimization problems

In this paper, we propose an interior-point method for linearly constrai...

0 Immanuel M. Bomze, et al. ∙

research

∙ 09/10/2018

Learning in time-varying games

In this paper, we examine the long-term behavior of regret-minimizing ag...

0 Benoit Duvocelle, et al. ∙

research

∙ 07/07/2018

Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile

Owing to their connection with generative adversarial networks (GANs), s...

0 Panayotis Mertikopoulos, et al. ∙

research

∙ 07/07/2018

Mirror descent in saddle-point problems: Going the extra (gradient) mile

Owing to their connection with generative adversarial networks (GANs), s...

0 Panayotis Mertikopoulos, et al. ∙

research

∙ 04/12/2018

Online convex optimization and no-regret learning: Algorithms, guarantees and applications

Spurred by the enthusiasm surrounding the "Big Data" paradigm, the mathe...

0 E. Veronica Belmega, et al. ∙

research

∙ 09/08/2017

Cycles in adversarial regularized learning

Regularized learning is a fundamental technique in online optimization, ...

0 Panayotis Mertikopoulos, et al. ∙

research

∙ 12/20/2014

On the robustness of learning in games with stochastically perturbed payoff observations

Motivated by the scarcity of accurate payoff feedback in practical appli...

0 Mario Bravo, et al. ∙

research

∙ 12/01/2014

Game-theoretical control with continuous action sets

Motivated by the recent applications of game-theoretical learning techni...

0 Steven Perkins, et al. ∙

research

∙ 01/27/2014

A continuous-time approach to online optimization

We consider a family of learning strategies for online optimization prob...

0 Joon Kwon, et al. ∙

Panayotis Mertikopoulos

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