2k citations · 6.6k across the 78 of their papers we have counts for
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Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C++, and soon more
Julien Mairal
Cyanure is an open-source C++ software package with a Python interface. The goal of Cyanure is to provide state-of-the-art solvers for learning linear models, based on stochastic v…
Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions
Grégoire Mialon, Alexandre d'Aspremont, Julien Mairal
We design simple screening tests to automatically discard data samples in empirical risk minimization without losing optimization guarantees. We derive loss functions that produce…
Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration
Bruno Lecouat, Jean Ponce, Julien Mairal
Non-local self-similarity and sparsity principles have proven to be powerful priors for natural image modeling. We propose a novel differentiable relaxation of joint sparsity that…
Recurrent Kernel Networks
Dexiong Chen, Laurent Jacob, Julien Mairal
Substring kernels are classical tools for representing biological sequences or text. However, when large amounts of annotated data are available, models that allow end-to-end train…
A Generic Acceleration Framework for Stochastic Composite Optimization
Andrei Kulunchakov, Julien Mairal
In this paper, we introduce various mechanisms to obtain accelerated first-order stochastic optimization algorithms when the objective function is convex or strongly convex. Specif…
Estimate Sequences for Variance-Reduced Stochastic Composite Optimization
Andrei Kulunchakov, Julien Mairal
In this paper, we propose a unified view of gradient-based algorithms for stochastic convex composite optimization by extending the concept of estimate sequence introduced by Neste…
On the Inductive Bias of Neural Tangent Kernels
Alberto Bietti, Julien Mairal
State-of-the-art neural networks are heavily over-parameterized, making the optimization algorithm a crucial ingredient for learning predictive models with good generalization prop…
Unsupervised Pre-Training of Image Features on Non-Curated Data
Mathilde Caron, Piotr Bojanowski, Julien Mairal +1
Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervi…
Diversity with Cooperation: Ensemble Methods for Few-Shot Classification
Nikita Dvornik, Cordelia Schmid, Julien Mairal
Few-shot classification consists of learning a predictive model that is able to effectively adapt to a new class, given only a few annotated samples. To solve this challenging prob…
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise
Andrei Kulunchakov, Julien Mairal
In this paper, we propose a unified view of gradient-based algorithms for stochastic convex composite optimization by extending the concept of estimate sequence introduced by Neste…