185 citations
- École Normale Supérieure - PSLFR7 papers
- Département d'InformatiqueFR6 papers
- Centre National de la Recherche ScientifiqueFR2 papers
- Institut national de recherche en sciences et technologies du numériqueFR2 papers
- Institut Polytechnique de ParisFR2 papers
- Laboratoire Traitement et Communication de l’InformationFR2 papers
- Mila - Quebec Artificial Intelligence InstituteCA2 papers
- Télécom ParisFR2 papers
- Aalto UniversityFI1 paper
- Centre de Mathématiques Appliquées de l'École polytechniqueFR1 paper
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- Facility for Antiproton and Ion ResearchDE1 paper
12 papers
Nonlinear conjugate gradient methods: worst-case convergence rates via computer-assisted analyses
Shuvomoy Das Gupta, Robert M. Freund, Xu Andy Sun +1
We propose a computer-assisted approach to the analysis of the worst-case convergence of nonlinear conjugate gradient methods (NCGMs). Those methods are known for their generally g…
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity
Eduard Gorbunov, Adrien Taylor, Samuel Horváth +1
Algorithms for min-max optimization and variational inequalities are often studied under monotonicity assumptions. Motivated by non-monotone machine learning applications, we follo…
Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization
Benjamin Dubois-Taine, Francis Bach, Quentin Berthet +1
We consider the problem of minimizing the sum of two convex functions. One of those functions has Lipschitz-continuous gradients, and can be accessed via stochastic oracles, wherea…
Super-Acceleration with Cyclical Step-sizes
Baptiste Goujaud, Damien Scieur, Aymeric Dieuleveut +2
We develop a convergence-rate analysis of momentum with cyclical step-sizes. We show that under some assumption on the spectral gap of Hessians in machine learning, cyclical step-s…
On the Consistency of Max-Margin Losses
Alex Nowak-Vila, Alessandro Rudi, Francis Bach
The foundational concept of Max-Margin in machine learning is ill-posed for output spaces with more than two labels such as in structured prediction. In this paper, we show that th…
Self-Supervised VQ-VAE for One-Shot Music Style Transfer
Ondřej Cífka, Alexey Ozerov, Umut Şimşekli +1
Neural style transfer, allowing to apply the artistic style of one image to another, has become one of the most widely showcased computer vision applications shortly after its intr…