735 citations · 992 across the 15 of their papers we have counts for
9 papers · 1 filter
Functional Bilevel Optimization for Machine Learning
Ieva Petrulionyte, Julien Mairal, Michael Arbel
In this paper, we introduce a new functional point of view on bilevel optimization problems for machine learning, where the inner objective is minimized over a function space. Thes…
Convolutional Kernel Networks for Graph-Structured Data
Dexiong Chen, Laurent Jacob, Julien Mairal
We introduce a family of multilayer graph kernels and establish new links between graph convolutional neural networks and kernel methods. Our approach generalizes convolutional ker…
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…
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…
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…