Search Results for author:
Found 20 papers, 3 papers with code
Contracting with a Learning Agent
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
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.
A Quadratic Speedup in Finding Nash Equilibria of Quantum Zero-Sum Games
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
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
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
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
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.
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games
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.
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces
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.
Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals
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 $n^{-O(\log k)}$.
On the Rate of Convergence of Regularized Learning in Games: From Bandits and Uncertainty to Optimism and Beyond
In this paper, we examine the convergence rate of a wide range of regularized methods for learning in games.
Towards convergence to Nash equilibria in two-team zero-sum games
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.
Teamwork makes von Neumann work:Min-Max Optimization in Two-Team Zero-Sum Games
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
This paper considers the following question: how well can depth-two ReLU networks with randomly initialized bottom-level weights represent smooth functions?
Solving Min-Max Optimization with Hidden Structure via Gradient Descent Ascent
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
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
No-regret learning and mixed Nash equilibria: They do not mix
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
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.
Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games
We study a wide class of non-convex non-concave min-max games that generalizes over standard bilinear zero-sum games.
Pattern Search Multidimensional Scaling
We present a novel view of nonlinear manifold learning using derivative-free optimization techniques.
