28 citations · 29 across the 11 of their papers we have counts for
15 papers
Krasnosel'skii-Mann iterations beyond asymptotics: a combinatorial analysis
Mario Bravo, Roberto Cominetti
We revisit the classical Krasnosel'skii-Mann fixed point iteration for contractions and nonexpansive maps in general normed spaces. This iteration is ubiquitous across a wide range…
On the occupation measure of evolution models with vanishing mutations
Michel Benaïm, Mario Bravo, Mathieu Faure
We study the almost sure convergence of the occupation measure of evolution models where mutation rates decrease over time. We show that if the mutation parameter vanishes at a con…
Minimax-optimal Halpern iterations for Lipschitz maps
Mario Bravo, Roberto Cominetti, Jongmin Lee
This paper investigates the minimax-optimality of Halpern fixed-point iterations for Lipschitz maps in general normed spaces. Starting from an a priori bound on the orbit of iterat…
Near-Optimal Sample Complexity for MDPs via Anchoring
Jongmin Lee, Mario Bravo, Roberto Cominetti
We study a new model-free algorithm to compute -optimal policies for average reward Markov decision processes, in the weakly communicating case. Given a generative mod…
Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity
Mario Bravo, Juan P. Flores-Mella, Cristóbal Guzmán
We study the mixing time of the projected Langevin algorithm (LA) and the privacy curve of noisy Stochastic Gradient Descent (SGD), beyond nonexpansive iterations. Specifically, we…
Stochastic Halpern iteration in normed spaces and applications to reinforcement learning
Mario Bravo, Juan Pablo Contreras
We analyze the oracle complexity of the stochastic Halpern iteration with minibatch, where we aim to approximate fixed-points of nonexpansive and contractive operators in a normed…
Stochastic fixed-point iterations for nonexpansive maps: Convergence and error bounds
Mario Bravo, Roberto Cominetti
We study a stochastically perturbed version of the well-known Krasnoselski--Mann iteration for computing fixed points of nonexpansive maps in finite dimensional normed spaces. We d…
Universal bounds for fixed point iterations via optimal transport metrics
Mario Bravo, Thierry Champion, Roberto Cominetti
We present a self-contained analysis of a particular family of metrics over the set of non-negative integers. We show that these metrics, which are defined through a nested sequenc…
Bandit learning in concave -person games
Mario Bravo, David S. Leslie, Panayotis Mertikopoulos
This paper examines the long-run behavior of learning with bandit feedback in non-cooperative concave games. The bandit framework accounts for extremely low-information environment…
Learning with minimal information in continuous games
Sebastian Bervoets, Mario Bravo, Mathieu Faure
We introduce a stochastic learning process called the dampened gradient approximation process. While learning models have almost exclusively focused on finite games, in this paper…
Rates of convergence for inexact Krasnosel'skii-Mann iterations in Banach spaces
Mario Bravo, Roberto Cominetti, Matías Pavez-Signé
We study the convergence of an inexact version of the classical Krasnosel'skii-Mann iteration for computing fixed points of nonexpansive maps. Our main result establishes a new met…
Sharp convergence rates for averaged nonexpansive maps
Mario Bravo, Roberto Cominetti
We establish sharp estimates for the convergence rate of the Kranosel'ski\vı-Mann fixed point iteration in general normed spaces, and we use them to show that the asymptotic regula…
On the robustness of learning in games with stochastically perturbed payoff observations
Mario Bravo, Panayotis Mertikopoulos
Motivated by the scarcity of accurate payoff feedback in practical applications of game theory, we examine a class of learning dynamics where players adjust their choices based on…
Reinforcement learning with restrictions on the action set
Mario Bravo, Mathieu Faure
Consider a 2-player normal-form game repeated over time. We introduce an adaptive learning procedure, where the players only observe their own realized payoff at each stage. We ass…
An adjusted payoff-based procedure for normal form games
Mario Bravo
We study a simple adaptive model in the framework of an N -player normal form game. The model consists of a repeated game where the players only know their own action space and the…