b'Francis Bach'

research

∙ 07/24/2023

Nonparametric Linear Feature Learning in Regression Through Regularisation

Representation learning plays a crucial role in automated feature select...

0 Bertille Follain, et al. ∙

research

∙ 06/28/2023

Theory and applications of the Sum-Of-Squares technique

The Sum-of-Squares (SOS) approximation method is a technique used in opt...

0 Francis Bach, et al. ∙

research

∙ 05/31/2023

Chain of Log-Concave Markov Chains

Markov chain Monte Carlo (MCMC) is a class of general-purpose algorithms...

0 Saeed Saremi, et al. ∙

research

∙ 05/28/2023

On the impact of activation and normalization in obtaining isometric embeddings at initialization

In this paper, we explore the structure of the penultimate Gram matrix i...

0 Amir Joudaki, et al. ∙

research

∙ 03/24/2023

The limited-memory recursive variational Gaussian approximation (L-RVGA)

We consider the problem of computing a Gaussian approximation to the pos...

0 Marc Lambert, et al. ∙

research

∙ 03/21/2023

Universal Smoothed Score Functions for Generative Modeling

We consider the problem of generative modeling based on smoothing an unk...

0 Saeed Saremi, et al. ∙

research

∙ 03/16/2023

Variational Principles for Mirror Descent and Mirror Langevin Dynamics

Mirror descent, introduced by Nemirovski and Yudin in the 1970s, is a pr...

0 Belinda Tzen, et al. ∙

research

∙ 03/06/2023

Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation

Sampling from Gibbs distributions p(x) ∝exp(-V(x)/ε) and computing their...

0 David Holzmüller, et al. ∙

research

∙ 03/02/2023

High-dimensional analysis of double descent for linear regression with random projections

We consider linear regression problems with a varying number of random p...

0 Francis Bach, et al. ∙

research

∙ 02/13/2023

Kernelized Diffusion maps

Spectral clustering and diffusion maps are celebrated dimensionality red...

0 Loucas Pillaud-Vivien, et al. ∙

research

∙ 02/07/2023

Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy

We present an algorithm for minimizing an objective with hard-to-compute...

0 Blake Woodworth, et al. ∙

research

∙ 02/07/2023

On the relationship between multivariate splines and infinitely-wide neural networks

We consider multivariate splines and show that they have a random featur...

0 Francis Bach, et al. ∙

research

∙ 11/10/2022

Regression as Classification: Influence of Task Formulation on Neural Network Features

Neural networks can be trained to solve regression problems by using gra...

0 Lawrence Stewart, et al. ∙

research

∙ 09/19/2022

On the Theoretical Properties of Noise Correlation in Stochastic Optimization

Studying the properties of stochastic noise to optimize complex non-conv...

0 Aurelien Lucchi, et al. ∙

research

∙ 06/27/2022

Sum-of-Squares Relaxations for Information Theory and Variational Inference

We consider extensions of the Shannon relative entropy, referred to as f...

0 Francis Bach, et al. ∙

research

∙ 06/15/2022

Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays

The existing analysis of asynchronous stochastic gradient descent (SGD) ...

0 Konstantin Mishchenko, et al. ∙

research

∙ 06/09/2022

Explicit Regularization in Overparametrized Models via Noise Injection

Injecting noise within gradient descent has several desirable features. ...

0 Antonio Orvieto, et al. ∙

research

∙ 05/31/2022

Variational inference via Wasserstein gradient flows

Along with Markov chain Monte Carlo (MCMC) methods, variational inferenc...

0 Marc Lambert, et al. ∙

research

∙ 05/26/2022

Active Labeling: Streaming Stochastic Gradients

The workhorse of machine learning is stochastic gradient descent. To acc...

0 Vivien Cabannes, et al. ∙

research

∙ 05/25/2022

Entropy Maximization with Depth: A Variational Principle for Random Neural Networks

To understand the essential role of depth in neural networks, we investi...

0 Amir Joudaki, et al. ∙

research

∙ 05/25/2022

A systematic approach to Lyapunov analyses of continuous-time models in convex optimization

First-order methods are often analyzed via their continuous-time models,...

0 Céline Moucer, et al. ∙

research

∙ 05/25/2022

Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization

We consider the problem of minimizing the sum of two convex functions. O...

0 Benjamin Dubois-Taine, et al. ∙

research

∙ 04/16/2022

Polynomial-time sparse measure recovery

How to recover a probability measure with sparse support from particular...

0 Hadi Daneshmand, et al. ∙

research

∙ 04/11/2022

Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares

We consider potentially non-convex optimization problems, for which opti...

0 Blake Woodworth, et al. ∙

research

∙ 02/17/2022

Information Theory with Kernel Methods

We consider the analysis of probability distributions through their asso...

0 Francis Bach, et al. ∙

research

∙ 02/16/2022

On a Variance Reduction Correction of the Temporal Difference for Policy Evaluation in the Stochastic Continuous Setting

This paper deals with solving continuous time, state and action optimiza...

0 Ziad Kobeissi, et al. ∙

research

∙ 02/06/2022

Anticorrelated Noise Injection for Improved Generalization

Injecting artificial noise into gradient descent (GD) is commonly employ...

0 Antonio Orvieto, et al. ∙

research

∙ 01/28/2022

Differential Privacy Guarantees for Stochastic Gradient Langevin Dynamics

We analyse the privacy leakage of noisy stochastic gradient descent by m...

0 Theo Ryffel, et al. ∙

research

∙ 10/29/2021

Convergence of Uncertainty Sampling for Active Learning

Uncertainty sampling in active learning is heavily used in practice to r...

0 Anant Raj, et al. ∙

research

∙ 10/20/2021

Sampling from Arbitrary Functions via PSD Models

In many areas of applied statistics and machine learning, generating an ...

0 Ulysse Marteau-Ferey, et al. ∙

research

∙ 10/15/2021

Gradient Descent on Infinitely Wide Neural Networks: Global Convergence and Generalization

Many supervised machine learning methods are naturally cast as optimizat...

0 Francis Bach, et al. ∙

research

∙ 07/02/2021

Screening for a Reweighted Penalized Conditional Gradient Method

The conditional gradient method (CGM) is widely used in large-scale spar...

0 Yifan Sun, et al. ∙

research

∙ 06/10/2021

A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip

We introduce the continuized Nesterov acceleration, a close variant of N...

0 Mathieu Even, et al. ∙

research

∙ 06/07/2021

Batch Normalization Orthogonalizes Representations in Deep Random Networks

This paper underlines a subtle property of batch-normalization (BN): Suc...

0 Hadi Daneshmand, et al. ∙

research

∙ 05/31/2021

Max-Margin is Dead, Long Live Max-Margin!

The foundational concept of Max-Margin in machine learning is ill-posed ...

0 Alex Nowak-Vila, et al. ∙

research

∙ 02/11/2021

A Continuized View on Nesterov Acceleration

We introduce the "continuized" Nesterov acceleration, a close variant of...

0 Raphaël Berthier, et al. ∙

research

∙ 02/04/2021

Disambiguation of weak supervision with exponential convergence rates

Machine learning approached through supervised learning requires expensi...

0 Vivien Cabannes, et al. ∙

research

∙ 02/01/2021

Fast rates in structured prediction

Discrete supervised learning problems such as classification are often t...

0 Vivien Cabannes, et al. ∙

research

∙ 12/22/2020

Finding Global Minima via Kernel Approximations

We consider the global minimization of smooth functions based solely on ...

0 Alessandro Rudi, et al. ∙

research

∙ 10/02/2020

Variance-Reduced Methods for Machine Learning

Stochastic optimization lies at the heart of machine learning, and its c...

20 Robert M. Gower, et al. ∙

research

∙ 09/30/2020

Deep Equals Shallow for ReLU Networks in Kernel Regimes

Deep networks are often considered to be more expressive than shallow on...

7 Alberto Bietti, et al. ∙

research

∙ 07/08/2020

Non-parametric Models for Non-negative Functions

Linear models have shown great effectiveness and flexibility in many fie...

8 Ulysse Marteau-Ferey, et al. ∙

research

∙ 07/02/2020

Consistent Structured Prediction with Max-Min Margin Markov Networks

Max-margin methods for binary classification such as the support vector ...

19 Alex Nowak-Vila, et al. ∙

research

∙ 06/25/2020

Dual-Free Stochastic Decentralized Optimization with Variance Reduction

We consider the problem of training machine learning models on distribut...

0 Hadrien Hendrikx, et al. ∙

research

∙ 06/16/2020

Structured and Localized Image Restoration

We present a novel approach to image restoration that leverages ideas fr...

10 Thomas Eboli, et al. ∙

research

∙ 06/15/2020

Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model

In the context of statistical supervised learning, the noiseless linear ...

0 Raphaël Berthier, et al. ∙

research

∙ 06/10/2020

Principled Analyses and Design of First-Order Methods with Inexact Proximal Operators

Proximal operations are among the most common primitives appearing in bo...

0 Mathieu Barré, et al. ∙

research

∙ 06/08/2020

ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing

We propose ARIANN, a low-interaction framework to perform private traini...

6 Theo Ryffel, et al. ∙

research

∙ 05/20/2020

An Optimal Algorithm for Decentralized Finite Sum Optimization

Modern large-scale finite-sum optimization relies on two key aspects: di...

0 Hadrien Hendrikx, et al. ∙

research

∙ 03/30/2020

Explicit Regularization of Stochastic Gradient Methods through Duality

We consider stochastic gradient methods under the interpolation regime w...

0 Anant Raj, et al. ∙

Francis Bach

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