Search Results for author: Emmanouil-Vasileios Vlatakis-Gkaragkounis

Found 20 papers, 3 papers with code

Contracting with a Learning Agent

no code implementations29 Jan 2024 Guru Guruganesh, Yoav Kolumbus, Jon Schneider, Inbal Talgam-Cohen, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Joshua R. Wang, S. Matthew Weinberg

We initiate the study of repeated contracts with a learning agent, focusing on agents who achieve no-regret outcomes.

Exploiting hidden structures in non-convex games for convergence to Nash equilibrium

no code implementations NeurIPS 2023 Iosif Sakos, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos, Georgios Piliouras

A wide array of modern machine learning applications - from adversarial models to multi-agent reinforcement learning - can be formulated as non-cooperative games whose Nash equilibria represent the system's desired operational states.

Multi-agent Reinforcement Learning

A Quadratic Speedup in Finding Nash Equilibria of Quantum Zero-Sum Games

no code implementations17 Nov 2023 Francisca Vasconcelos, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos, Georgios Piliouras, Michael I. Jordan

In 2008, Jain and Watrous proposed the first classical algorithm for computing equilibria in quantum zero-sum games using the Matrix Multiplicative Weight Updates (MMWU) method to achieve a convergence rate of $\mathcal{O}(d/\epsilon^2)$ iterations to $\epsilon$-Nash equilibria in the $4^d$-dimensional spectraplex.

Curvature-Independent Last-Iterate Convergence for Games on Riemannian Manifolds

no code implementations29 Jun 2023 Yang Cai, Michael I. Jordan, Tianyi Lin, Argyris Oikonomou, Emmanouil-Vasileios Vlatakis-Gkaragkounis

Numerous applications in machine learning and data analytics can be formulated as equilibrium computation over Riemannian manifolds.

Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements

no code implementations28 Jun 2023 Emmanouil-Vasileios Vlatakis-Gkaragkounis, Angeliki Giannou, Yudong Chen, Qiaomin Xie

Our work endeavors to elucidate and quantify the probabilistic structures intrinsic to these algorithms.

Chaos persists in large-scale multi-agent learning despite adaptive learning rates

no code implementations1 Jun 2023 Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Georgios Piliouras

Although such techniques are known to allow for improved convergence guarantees in small games, it has been much harder to analyze them in more relevant settings with large populations of agents.

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

no code implementations17 Oct 2022 Angeliki Giannou, Kyriakos Lotidis, Panayotis Mertikopoulos, Emmanouil-Vasileios Vlatakis-Gkaragkounis

Learning in stochastic games is a notoriously difficult problem because, in addition to each other's strategic decisions, the players must also contend with the fact that the game itself evolves over time, possibly in a very complicated manner.

Policy Gradient Methods

Efficiently Computing Nash Equilibria in Adversarial Team Markov Games

no code implementations3 Aug 2022 Fivos Kalogiannis, Ioannis Anagnostides, Ioannis Panageas, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Vaggos Chatziafratis, Stelios Stavroulakis

In this work, we depart from those prior results by investigating infinite-horizon \emph{adversarial team Markov games}, a natural and well-motivated class of games in which a team of identically-interested players -- in the absence of any explicit coordination or communication -- is competing against an adversarial player.

Multi-agent Reinforcement Learning

First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces

no code implementations4 Jun 2022 Michael I. Jordan, Tianyi Lin, Emmanouil-Vasileios Vlatakis-Gkaragkounis

From optimal transport to robust dimensionality reduction, a plethora of machine learning applications can be cast into the min-max optimization problems over Riemannian manifolds.

Dimensionality Reduction

Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals

no code implementations10 Feb 2022 Daniel Hsu, Clayton Sanford, Rocco Servedio, Emmanouil-Vasileios Vlatakis-Gkaragkounis

This lower bound is essentially best possible since an SQ algorithm of Klivans et al. (2008) agnostically learns this class to any constant excess error using $n^{O(\log k)}$ queries of tolerance $n^{-O(\log k)}$.

Towards convergence to Nash equilibria in two-team zero-sum games

no code implementations7 Nov 2021 Fivos Kalogiannis, Ioannis Panageas, Emmanouil-Vasileios Vlatakis-Gkaragkounis

On a brighter note, we propose a first-order method that leverages control theory techniques and under some conditions enjoys last-iterate local convergence to a Nash equilibrium.

Vocal Bursts Valence Prediction

Teamwork makes von Neumann work:Min-Max Optimization in Two-Team Zero-Sum Games

no code implementations29 Sep 2021 Fivos Kalogiannis, Ioannis Panageas, Emmanouil-Vasileios Vlatakis-Gkaragkounis

Motivated by recent advances in both theoretical and applied aspects of multiplayer games, spanning from e-sports to multi-agent generative adversarial networks, we focus on min-max optimization in team zero-sum games.

On the Approximation Power of Two-Layer Networks of Random ReLUs

no code implementations3 Feb 2021 Daniel Hsu, Clayton Sanford, Rocco A. Servedio, Emmanouil-Vasileios Vlatakis-Gkaragkounis

This paper considers the following question: how well can depth-two ReLU networks with randomly initialized bottom-level weights represent smooth functions?

Vocal Bursts Valence Prediction

Solving Min-Max Optimization with Hidden Structure via Gradient Descent Ascent

no code implementations NeurIPS 2021 Lampros Flokas, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Georgios Piliouras

Inspired by this, we study standard gradient descent ascent (GDA) dynamics in a specific class of non-convex non-concave zero-sum games, that we call hidden zero-sum games.

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

no code implementations12 Jan 2021 Angeliki Giannou, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos

This equivalence extends existing continuous-time versions of the folk theorem of evolutionary game theory to a bona fide algorithmic learning setting, and it provides a clear refinement criterion for the prediction of the day-to-day behavior of no-regret learning in games

Multi-Armed Bandits

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

no code implementations NeurIPS 2020 Lampros Flokas, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Thanasis Lianeas, Panayotis Mertikopoulos, Georgios Piliouras

Understanding the behavior of no-regret dynamics in general $N$-player games is a fundamental question in online learning and game theory.

Efficiently avoiding saddle points with zero order methods: No gradients required

1 code implementation NeurIPS 2019 Lampros Flokas, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Georgios Piliouras

We consider the case of derivative-free algorithms for non-convex optimization, also known as zero order algorithms, that use only function evaluations rather than gradients.

Pattern Search Multidimensional Scaling

1 code implementation1 Jun 2018 Georgios Paraskevopoulos, Efthymios Tzinis, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Alexandros Potamianos

We present a novel view of nonlinear manifold learning using derivative-free optimization techniques.

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