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02/05/2023
Learning in quantum games
In this paper, we introduce a class of learning dynamics for general qua...
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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...
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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...
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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,...
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09/17/2022
Survival of dominated strategies under imitation dynamics
The literature on evolutionary game theory suggests that pure strategies...
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09/11/2022
Learning in Games with Quantized Payoff Observations
This paper investigates the impact of feedback quantization on multi-age...
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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...
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06/19/2022
Nested bandits
In many online decision processes, the optimizing agent is called to cho...
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06/14/2022
The Dynamics of Riemannian Robbins-Monro Algorithms
Many important learning algorithms, such as stochastic gradient methods,...
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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...
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06/08/2022
Learning in games from a stochastic approximation viewpoint
We develop a unified stochastic approximation framework for analyzing th...
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01/09/2022
Routing in an Uncertain World: Adaptivity, Efficiency, and Equilibrium
We consider the traffic assignment problem in nonatomic routing games wh...
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09/13/2021
Zeroth-order non-convex learning via hierarchical dual averaging
We propose a hierarchical version of dual averaging for zeroth-order onl...
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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...
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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...
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07/06/2021
Distributed stochastic optimization with large delays
One of the most widely used methods for solving large-scale stochastic o...
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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...
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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...
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06/28/2021
Asymptotic Degradation of Linear Regression Estimates With Strategic Data Sources
We consider the problem of linear regression from strategic data sources...
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05/27/2021
Optimization in Open Networks via Dual Averaging
In networks of autonomous agents (e.g., fleets of vehicles, scattered se...
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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 ...
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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...
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12/21/2020
Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism
Online learning has been successfully applied to many problems in which ...
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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...
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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...
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10/16/2020
Online non-convex optimization with imperfect feedback
We consider the problem of online learning with non-convex losses. In te...
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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...
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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...
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06/19/2020
Gradient-free Online Learning in Games with Delayed Rewards
Motivated by applications to online advertising and recommender systems,...
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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...
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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...
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03/23/2020
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling
Owing to their stability and convergence speed, extragradient methods ha...
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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...
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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 ...
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01/02/2020
Quick or cheap? Breaking points in dynamic markets
We examine two-sided markets where players arrive stochastically over ti...
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08/22/2019
On the convergence of single-call stochastic extra-gradient methods
Variational inequalities have recently attracted considerable interest i...
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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...
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10/03/2018
Bandit learning in concave N-person games
This paper examines the long-run behavior of learning with bandit feedba...
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09/25/2018
Hessian barrier algorithms for linearly constrained optimization problems
In this paper, we propose an interior-point method for linearly constrai...
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09/10/2018
Learning in time-varying games
In this paper, we examine the long-term behavior of regret-minimizing ag...
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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...
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07/07/2018
Mirror descent in saddle-point problems: Going the extra (gradient) mile
Owing to their connection with generative adversarial networks (GANs), s...
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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...
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09/08/2017
Cycles in adversarial regularized learning
Regularized learning is a fundamental technique in online optimization, ...
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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...
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12/01/2014
Game-theoretical control with continuous action sets
Motivated by the recent applications of game-theoretical learning techni...
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01/27/2014