
Adaptive firstorder methods revisited: Convex optimization without Lipschitz requirements
We propose a new family of adaptive firstorder methods for a class of c...
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Distributed stochastic optimization with large delays
One of the most widely used methods for solving largescale stochastic o...
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The LastIterate 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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Learning in nonatomic games, Part I: Finite action spaces and population games
We examine the longrun behavior of a wide range of dynamics for learnin...
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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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Optimization in Open Networks via Dual Averaging
In networks of autonomous agents (e.g., fleets of vehicles, scattered se...
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Adaptive Learning in Continuous Games: Optimal Regret Bounds and Convergence to Nash Equilibrium
In gametheoretic learning, several agents are simultaneously following ...
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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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MultiAgent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism
Online learning has been successfully applied to many problems in which ...
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Adaptive extragradient methods for minmax optimization and games
We present a new family of minmax optimization algorithms that automati...
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Noregret learning and mixed Nash equilibria: They do not mix
Understanding the behavior of noregret dynamics in general Nplayer gam...
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Online nonconvex optimization with imperfect feedback
We consider the problem of online learning with nonconvex losses. In te...
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Regret minimization in stochastic nonconvex learning via a proximalgradient approach
Motivated by applications in machine learning and operations research, w...
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On the Almost Sure Convergence of Stochastic Gradient Descent in NonConvex Problems
This paper analyzes the trajectories of stochastic gradient descent (SGD...
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Gradientfree Online Learning in Games with Delayed Rewards
Motivated by applications to online advertising and recommender systems,...
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The limits of minmax optimization algorithms: convergence to spurious noncritical sets
Compared to minimization problems, the minmax landscape in machine lear...
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Fast GradientFree Optimization in Distributed MultiUser MIMO Systems
In this paper, we develop a gradientfree optimization methodology for e...
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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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A new regret analysis for Adamtype algorithms
In this paper, we focus on a theorypractice gap for Adam and its varian...
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FiniteTime LastIterate Convergence for MultiAgent Learning in Games
We consider multiagent learning via online gradient descent (OGD) in a ...
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Quick or cheap? Breaking points in dynamic markets
We examine twosided markets where players arrive stochastically over ti...
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On the convergence of singlecall stochastic extragradient methods
Variational inequalities have recently attracted considerable interest i...
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Forwardbackwardforward methods with variance reduction for stochastic variational inequalities
We develop a new stochastic algorithm with variance reduction for solvin...
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Bandit learning in concave Nperson games
This paper examines the longrun behavior of learning with bandit feedba...
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Hessian barrier algorithms for linearly constrained optimization problems
In this paper, we propose an interiorpoint method for linearly constrai...
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Learning in timevarying games
In this paper, we examine the longterm behavior of regretminimizing ag...
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Optimistic mirror descent in saddlepoint problems: Going the extra (gradient) mile
Owing to their connection with generative adversarial networks (GANs), s...
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Mirror descent in saddlepoint problems: Going the extra (gradient) mile
Owing to their connection with generative adversarial networks (GANs), s...
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Online convex optimization and noregret learning: Algorithms, guarantees and applications
Spurred by the enthusiasm surrounding the "Big Data" paradigm, the mathe...
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Cycles in adversarial regularized learning
Regularized learning is a fundamental technique in online optimization, ...
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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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Gametheoretical control with continuous action sets
Motivated by the recent applications of gametheoretical learning techni...
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A continuoustime approach to online optimization
We consider a family of learning strategies for online optimization prob...
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Panayotis Mertikopoulos
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