Publications

Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS

We aim to optimize a black-box function f : X→ R under the assumption that f is H¨older smooth and has bounded norm in the Reproducing …

Adaptive Sampling for Estimating Multiple Probability Distributions

We consider the problem of allocating samples to a finite set of discrete distributions in order to learn them uniformly well in terms …

Active learning for binary classification with abstention

We construct and analyze active learning algorithms for the problem of binary classification with abstention. We consider three …

Active Model Estimation in Markov Decision Processes

We study the problem of efficient exploration in order to learn an accurate model of an environment, modeled as a Markov decision …

Multiscale Gaussian Process Level Set Estimation

In this paper, the problem of estimating the level set of a black-box function from noisy and expensive evaluation queries is …

Fully decentralized federated learning

We consider the problem of training a machine learning model over a network of users in a fully decentralized framework. The users take …

Gaussian process bandits with adaptive discretization

In this paper, the problem of maximizing a black-box function f:X→R is studied in the Bayesian framework with a Gaussian Process prior. …

Species tree estimation using ASTRAL: how many genes are enough?

Species tree reconstruction from genomic data is increasingly performed using methods that account for sources of gene tree discordance …