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Picture for Mathieu Blondel

Mathieu Blondel

DMA, CNRS

Sparse Continuous Distributions and Fenchel-Young Losses


Aug 04, 2021
André F. T. Martins, Marcos Treviso, António Farinhas, Pedro M. Q. Aguiar, Mário A. T. Figueiredo, Mathieu Blondel, Vlad Niculae

* arXiv admin note: text overlap with arXiv:2006.07214 

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Efficient and Modular Implicit Differentiation


May 31, 2021
Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert


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Implicit differentiation for fast hyperparameter selection in non-smooth convex learning


May 17, 2021
Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon


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Self-Supervised Learning of Audio Representations from Permutations with Differentiable Ranking


Mar 17, 2021
Andrew N Carr, Quentin Berthet, Mathieu Blondel, Olivier Teboul, Neil Zeghidour


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Momentum Residual Neural Networks


Feb 15, 2021
Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré

* 34 pages 

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Differentiable Divergences Between Time Series


Oct 16, 2020
Mathieu Blondel, Arthur Mensch, Jean-Philippe Vert


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Implicit differentiation of Lasso-type models for hyperparameter optimization


Feb 20, 2020
Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon


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Fast Differentiable Sorting and Ranking


Feb 20, 2020
Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga


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Learning with Differentiable Perturbed Optimizers


Feb 20, 2020
Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis Bach


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Structured Prediction with Projection Oracles


Oct 24, 2019
Mathieu Blondel

* In proceedings of NeurIPS 2019 

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Geometric Losses for Distributional Learning


May 15, 2019
Arthur Mensch, Mathieu Blondel, Gabriel Peyré

* Proceedings of the International Conference on Machine Learning, 2019, Long Beach, United States 

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Learning with Fenchel-Young Losses


Jan 08, 2019
Mathieu Blondel, André F. T. Martins, Vlad Niculae

* Extended version of arXiv:1805.09717 

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Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms


Oct 05, 2018
Mathieu Blondel, André F. T. Martins, Vlad Niculae


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SparseMAP: Differentiable Sparse Structured Inference


Jun 20, 2018
Vlad Niculae, André F. T. Martins, Mathieu Blondel, Claire Cardie

* Published in ICML 2018. 14 pages, including appendix 

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Scikit-learn: Machine Learning in Python


Jun 05, 2018
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Andreas Müller, Joel Nothman, Gilles Louppe, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay

* Journal of Machine Learning Research (2011) 
* Update authors list and URLs 

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Large-Scale Optimal Transport and Mapping Estimation


Feb 26, 2018
Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel

* 15 pages, 4 figures. To appear in the Proceedings of the International Conference on Learning Representations (ICLR) 2018 

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Soft-DTW: a Differentiable Loss Function for Time-Series


Feb 20, 2018
Marco Cuturi, Mathieu Blondel

* Published in ICML 2017 

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Differentiable Dynamic Programming for Structured Prediction and Attention


Feb 20, 2018
Arthur Mensch, Mathieu Blondel


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Smooth and Sparse Optimal Transport


Feb 20, 2018
Mathieu Blondel, Vivien Seguy, Antoine Rolet

* Accepted to AISTATS 2018 

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Blind Source Separation with Optimal Transport Non-negative Matrix Factorization


Feb 15, 2018
Antoine Rolet, Vivien Seguy, Mathieu Blondel, Hiroshi Sawada

* 22 pages, 7 figures, 2 additional files 

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Multi-output Polynomial Networks and Factorization Machines


Nov 04, 2017
Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda

* Published at NIPS 2017. 17 pages, including appendix 

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A Regularized Framework for Sparse and Structured Neural Attention


Nov 03, 2017
Vlad Niculae, Mathieu Blondel

* Published in NIPS 2017. 23 pages, including appendix 

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Higher-Order Factorization Machines


Oct 14, 2016
Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata


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Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms


Jul 29, 2016
Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda


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API design for machine learning software: experiences from the scikit-learn project


Sep 01, 2013
Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake Vanderplas, Arnaud Joly, Brian Holt, Gaël Varoquaux

* European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (2013) 

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