Search Results for author: Quentin Berthet

Found 18 papers, 3 papers with code

Efficient and Modular Implicit Differentiation

1 code implementation31 May 2021 Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert

In this paper, we propose a unified, efficient and modular approach for implicit differentiation of optimization problems.

Meta-Learning

Shuffle to Learn: Self-supervised learning from permutations via differentiable ranking

no code implementations1 Jan 2021 Andrew N Carr, Quentin Berthet, Mathieu Blondel, Olivier Teboul, Neil Zeghidour

In particular, we also improve music understanding by reordering spectrogram patches in the frequency space, as well as video classification by reordering frames along the time axis.

General Classification Self-Supervised Learning +1

Learning with Differentiable Pertubed Optimizers

no code implementations NeurIPS 2020 Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis Bach

Machine learning pipelines often rely on optimizers procedures to make discrete decisions (e. g., sorting, picking closest neighbors, or shortest paths).

Structured Prediction

Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design

no code implementations26 Apr 2020 Marco Cuturi, Olivier Teboul, Quentin Berthet, Arnaud Doucet, Jean-Philippe Vert

Our goal in this paper is to propose new group testing algorithms that can operate in a noisy setting (tests can be mistaken) to decide adaptively (looking at past results) which groups to test next, with the goal to converge to a good detection, as quickly, and with as few tests as possible.

Experimental Design

Stochastic Optimization for Regularized Wasserstein Estimators

no code implementations ICML 2020 Marin Ballu, Quentin Berthet, Francis Bach

We show that this algorithm can be extended to other tasks, including estimation of Wasserstein barycenters.

Stochastic Optimization

Learning with Differentiable Perturbed Optimizers

no code implementations20 Feb 2020 Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis Bach

Machine learning pipelines often rely on optimization procedures to make discrete decisions (e. g., sorting, picking closest neighbors, or shortest paths).

Structured Prediction

Fast Differentiable Sorting and Ranking

2 code implementations ICML 2020 Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga

While numerous works have proposed differentiable proxies to sorting and ranking, they do not achieve the $O(n \log n)$ time complexity one would expect from sorting and ranking operations.

Regularized Contextual Bandits

no code implementations11 Oct 2018 Xavier Fontaine, Quentin Berthet, Vianney Perchet

We consider the stochastic contextual bandit problem with additional regularization.

Multi-Armed Bandits

Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain

no code implementations6 Aug 2018 Quentin Berthet, Varun Kanade

We study the problem of hypothesis testing between two discrete distributions, where we only have access to samples after the action of a known reversible Markov chain, playing the role of noise.

Two-sample testing

Optimal link prediction with matrix logistic regression

no code implementations19 Mar 2018 Nicolai Baldin, Quentin Berthet

We consider the problem of link prediction, based on partial observation of a large network, and on side information associated to its vertices.

Link Prediction

Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe

no code implementations NeurIPS 2017 Quentin Berthet, Vianney Perchet

We consider the problem of bandit optimization, inspired by stochastic optimization and online learning problems with bandit feedback.

Stochastic Optimization

Average-case Hardness of RIP Certification

no code implementations NeurIPS 2016 Tengyao Wang, Quentin Berthet, Yaniv Plan

The restricted isometry property (RIP) for design matrices gives guarantees for optimal recovery in sparse linear models.

Detection of Planted Solutions for Flat Satisfiability Problems

no code implementations21 Feb 2015 Quentin Berthet, Jordan S. Ellenberg

We describe the properties of random instances of flat satisfiability, as well of the optimal rates of detection of the associated hypothesis testing problem.

Two-sample testing

Statistical and computational trade-offs in estimation of sparse principal components

no code implementations22 Aug 2014 Tengyao Wang, Quentin Berthet, Richard J. Samworth

In this paper, we show that, under a widely-believed assumption from computational complexity theory, there is a fundamental trade-off between statistical and computational performance in this problem.

Dimensionality Reduction

Computational Lower Bounds for Sparse PCA

no code implementations3 Apr 2013 Quentin Berthet, Philippe Rigollet

In the context of sparse principal component detection, we bring evidence towards the existence of a statistical price to pay for computational efficiency.

Optimal detection of sparse principal components in high dimension

no code implementations23 Feb 2012 Quentin Berthet, Philippe Rigollet

We perform a finite sample analysis of the detection levels for sparse principal components of a high-dimensional covariance matrix.

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