most citedBlack-box optimization of noisy functions with unknown smoothness

51 citations

6 papers

stat.ML202651 cited

Black-box optimization of noisy functions with unknown smoothness

Jean-Bastien Grill, Michal Valko, Rémi Munos

We study the problem of black-box optimization of a function f of any dimension, given function evaluations perturbed by noise. The function is assumed to be locally smooth around…

stat.ML2026

Active multiple matrix completion with adaptive confidence sets

Andrea Locatelli, Alexandra Carpentier, Michal Valko

In this work, we formulate a new multi-task active learning setting in which the learner's goal is to solve multiple matrix completion problems simultaneously. At each round, the l…

stat.ML20266 cited

A single algorithm for both restless and rested rotting bandits

Julien Seznec, Pierre Ménard, Alessandro Lazaric +1

In many application domains (e.g., recommender systems, intelligent tutoring systems), the rewards associated to the actions tend to decrease over time. This decay is either caused…

cs.LG202613 cited

On two ways to use determinantal point processes for Monte Carlo integration

Guillaume Gautier, Rémi Bardenet, Michal Valko

The standard Monte Carlo estimator of relies on independent samples from and has variance of order . Replacing the samples with…

cs.LG20264 cited

Planning in entropy-regularized Markov decision processes and games

Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard +2

We propose SmoothCruiser, a new planning algorithm for estimating the value function in entropy-regularized Markov decision processes and two-player games, given a generative model…

cs.LG20267 cited

Budgeted Online Influence Maximization

Pierre Perrault, Jennifer Healey, Zheng Wen +1

We introduce a new budgeted framework for online influence maximization, considering the total cost of an advertising campaign instead of the common cardinality constraint on a cho…