Rodolphe Jenatton

research

∙ 05/26/2023

Three Towers: Flexible Contrastive Learning with Pretrained Image Models

We introduce Three Towers (3T), a flexible method to improve the contras...

0 Jannik Kossen, et al. ∙

research

∙ 03/03/2023

When does Privileged Information Explain Away Label Noise?

Leveraging privileged information (PI), or features available during tra...

0 Guillermo Ortiz-Jiménez, et al. ∙

research

∙ 01/30/2023

Massively Scaling Heteroscedastic Classifiers

Heteroscedastic classifiers, which learn a multivariate Gaussian distrib...

0 Mark Collier, et al. ∙

research

∙ 10/19/2022

On the Adversarial Robustness of Mixture of Experts

Adversarial robustness is a key desirable property of neural networks. I...

0 Joan Puigcerver, et al. ∙

research

∙ 06/06/2022

Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts

Large sparsely-activated models have obtained excellent performance in m...

6 Basil Mustafa, et al. ∙

research

∙ 02/18/2022

Transfer and Marginalize: Explaining Away Label Noise with Privileged Information

Supervised learning datasets often have privileged information, in the f...

0 Mark Collier, et al. ∙

research

∙ 12/15/2021

Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach

Black box optimization requires specifying a search space to explore for...

0 Setareh Ariafar, et al. ∙

research

∙ 10/07/2021

Sparse MoEs meet Efficient Ensembles

Machine learning models based on the aggregated outputs of submodels, ei...

3 James Urquhart Allingham, et al. ∙

research

∙ 10/06/2021

Deep Classifiers with Label Noise Modeling and Distance Awareness

Uncertainty estimation in deep learning has recently emerged as a crucia...

0 Vincent Fortuin, et al. ∙

research

∙ 06/10/2021

Scaling Vision with Sparse Mixture of Experts

Sparsely-gated Mixture of Experts networks (MoEs) have demonstrated exce...

6 Carlos Riquelme, et al. ∙

research

∙ 06/07/2021

Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning

High-quality estimates of uncertainty and robustness are crucial for num...

0 Zachary Nado, et al. ∙

research

∙ 05/19/2021

Correlated Input-Dependent Label Noise in Large-Scale Image Classification

Large scale image classification datasets often contain noisy labels. We...

0 Mark Collier, et al. ∙

research

∙ 12/15/2020

Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization

Tuning complex machine learning systems is challenging. Machine learning...

0 Valerio Perrone, et al. ∙

research

∙ 12/15/2020

Amazon SageMaker Autopilot: a white box AutoML solution at scale

AutoML systems provide a black-box solution to machine learning problems...

0 Piali Das, et al. ∙

research

∙ 10/13/2020

Training independent subnetworks for robust prediction

Recent approaches to efficiently ensemble neural networks have shown tha...

0 Marton Havasi, et al. ∙

research

∙ 06/24/2020

Hyperparameter Ensembles for Robustness and Uncertainty Quantification

Ensembles over neural network weights trained from different random init...

13 Florian Wenzel, et al. ∙

research

∙ 06/10/2020

On Mixup Regularization

Mixup is a data augmentation technique that creates new examples as conv...

0 Luigi Carratino, et al. ∙

research

∙ 02/07/2020

The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks

Variational Bayesian Inference is a popular methodology for approximatin...

6 Jakub Świątkowski, et al. ∙

research

∙ 02/06/2020

How Good is the Bayes Posterior in Deep Neural Networks Really?

During the past five years the Bayesian deep learning community has deve...

27 Florian Wenzel, et al. ∙

research

∙ 01/14/2020

Hydra: Preserving Ensemble Diversity for Model Distillation

Ensembles of models have been empirically shown to improve predictive pe...

1 Linh Tran, et al. ∙

research

∙ 10/15/2019

Constrained Bayesian Optimization with Max-Value Entropy Search

Bayesian optimization (BO) is a model-based approach to sequentially opt...

0 Valerio Perrone, et al. ∙

research

∙ 09/27/2019

Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning

Bayesian optimization (BO) is a successful methodology to optimize black...

0 Valerio Perrone, et al. ∙

research

∙ 12/08/2017

Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start

Bayesian optimization (BO) is a model-based approach for gradient-free b...

0 Valerio Perrone, et al. ∙

research

∙ 02/17/2016

Online optimization and regret guarantees for non-additive long-term constraints

We consider online optimization in the 1-lookahead setting, where the ob...

0 Rodolphe Jenatton, et al. ∙

research

∙ 12/23/2015

Adaptive Algorithms for Online Convex Optimization with Long-term Constraints

We present an adaptive online gradient descent algorithm to solve online...

0 Rodolphe Jenatton, et al. ∙

research

∙ 07/19/2014

Sparse and spurious: dictionary learning with noise and outliers

A popular approach within the signal processing and machine learning com...

0 Rémi Gribonval, et al. ∙

research

∙ 03/20/2014

On The Sample Complexity of Sparse Dictionary Learning

In the synthesis model signals are represented as a sparse combinations ...

0 Matthias Seibert, et al. ∙

research

∙ 12/13/2013

Sample Complexity of Dictionary Learning and other Matrix Factorizations

Many modern tools in machine learning and signal processing, such as spa...

0 Rémi Gribonval, et al. ∙

research

∙ 10/02/2012

Local stability and robustness of sparse dictionary learning in the presence of noise

A popular approach within the signal processing and machine learning com...

0 Rodolphe Jenatton, et al. ∙

research

∙ 09/12/2011

Structured sparsity through convex optimization

Sparse estimation methods are aimed at using or obtaining parsimonious r...

0 Francis Bach, et al. ∙

research

∙ 08/03/2011

Optimization with Sparsity-Inducing Penalties

Sparse estimation methods are aimed at using or obtaining parsimonious r...

0 Francis Bach, et al. ∙

research

∙ 05/02/2011

Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity

Inverse inference, or "brain reading", is a recent paradigm for analyzin...

0 Rodolphe Jenatton, et al. ∙

research

∙ 04/11/2011

Convex and Network Flow Optimization for Structured Sparsity

We consider a class of learning problems regularized by a structured spa...

0 Julien Mairal, et al. ∙

research

∙ 09/11/2010

Proximal Methods for Hierarchical Sparse Coding

Sparse coding consists in representing signals as sparse linear combinat...

0 Rodolphe Jenatton, et al. ∙

research

∙ 08/31/2010

Network Flow Algorithms for Structured Sparsity

We consider a class of learning problems that involve a structured spars...

0 Julien Mairal, et al. ∙

research

∙ 09/08/2009

Structured Sparse Principal Component Analysis

We present an extension of sparse PCA, or sparse dictionary learning, wh...

0 Rodolphe Jenatton, et al. ∙

Rodolphe Jenatton

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