Mathieu Blondel

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 65

Top Topics

Efficient Optimization

Matrix Factorization

Source Separation

Structured Prediction

Top Venues

Publications

Tianlin Liu, Mathieu Blondel, Carlos Riquelme Ruiz, Joan Puigcerver
Routers in Vision Mixture of Experts: An Empirical Study. Trans. Mach. Learn. Res. 2024 (2024)
Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel
Decoding-time Realignment of Language Models. CoRR (2024)
Mathieu Blondel, Vincent Roulet
The Elements of Differentiable Programming. CoRR (2024)
Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet
Implicit Diffusion: Efficient Optimization through Stochastic Sampling. CoRR (2024)
Vincent Roulet, Atish Agarwala, Jean-Bastien Grill, Grzegorz Swirszcz, Mathieu Blondel, Fabian Pedregosa
Stepping on the Edge: Curvature Aware Learning Rate Tuners. CoRR (2024)
Seta Rakotomandimby, Jean-Philippe Chancelier, Michel De Lara, Mathieu Blondel
Learning with Fitzpatrick Losses. CoRR (2024)
Shangmin Guo, Biao Zhang, Tianlin Liu, Tianqi Liu, Misha Khalman, Felipe Llinares, Alexandre Ramé, Thomas Mesnard, Yao Zhao, Bilal Piot, Johan Ferret, Mathieu Blondel
Direct Language Model Alignment from Online AI Feedback. CoRR (2024)
Tianlin Liu, Mathieu Blondel, Carlos Riquelme, Joan Puigcerver
Routers in Vision Mixture of Experts: An Empirical Study. CoRR (2024)
Michael E. Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré
How do Transformers perform In-Context Autoregressive Learning? CoRR (2024)
Michael E. Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. CoRR (2023)
Tianlin Liu, Joan Puigcerver, Mathieu Blondel
Sparsity-Constrained Optimal Transport. ICLR (2023)
Michael Eli Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. ICML (2023)
Vincent Roulet, Mathieu Blondel
Dual Gauss-Newton Directions for Deep Learning. CoRR (2023)
Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert
Efficient and Modular Implicit Differentiation. NeurIPS (2022)
Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist
Learning Energy Networks with Generalized Fenchel-Young Losses. CoRR (2022)
André F. T. Martins, Marcos V. Treviso, António Farinhas, Pedro M. Q. Aguiar, Mário A. T. Figueiredo, Mathieu Blondel, Vlad Niculae
Sparse Continuous Distributions and Fenchel-Young Losses. J. Mach. Learn. Res. 23 (2022)
Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist
Learning Energy Networks with Generalized Fenchel-Young Losses. NeurIPS (2022)
Tianlin Liu, Joan Puigcerver, Mathieu Blondel
Sparsity-Constrained Optimal Transport. CoRR (2022)
Robert M. Gower, Mathieu Blondel, Nidham Gazagnadou, Fabian Pedregosa
Cutting Some Slack for SGD with Adaptive Polyak Stepsizes. CoRR (2022)
Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning. J. Mach. Learn. Res. 23 (2022)
Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré
Sinkformers: Transformers with Doubly Stochastic Attention. AISTATS (2022)
Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré
Momentum Residual Neural Networks. CoRR (2021)
Andrew N. Carr, Quentin Berthet, Mathieu Blondel, Olivier Teboul, Neil Zeghidour
Self-Supervised Learning of Audio Representations from Permutations with Differentiable Ranking. CoRR (2021)
Mathieu Blondel, Arthur Mensch, Jean-Philippe Vert
Differentiable Divergences Between Time Series. AISTATS (2021)
Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré
Momentum Residual Neural Networks. ICML (2021)
Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert
Efficient and Modular Implicit Differentiation. CoRR (2021)
Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning. CoRR (2021)
Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré
Sinkformers: Transformers with Doubly Stochastic Attention. CoRR (2021)
André F. T. Martins, Marcos V. Treviso, António Farinhas, Pedro M. Q. Aguiar, Mário A. T. Figueiredo, Mathieu Blondel, Vlad Niculae
Sparse Continuous Distributions and Fenchel-Young Losses. CoRR (2021)
Andrew N. Carr, Quentin Berthet, Mathieu Blondel, Olivier Teboul, Neil Zeghidour
Self-Supervised Learning of Audio Representations From Permutations With Differentiable Ranking. IEEE Signal Process. Lett. 28 (2021)
Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
Implicit differentiation of Lasso-type models for hyperparameter optimization. CoRR (2020)
Mathieu Blondel, André F. T. Martins, Vlad Niculae
Learning with Fenchel-Young losses. J. Mach. Learn. Res. 21 (2020)
Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga
Fast Differentiable Sorting and Ranking. CoRR (2020)
Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga
Fast Differentiable Sorting and Ranking. ICML (2020)
Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
Implicit differentiation of Lasso-type models for hyperparameter optimization. ICML (2020)
Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis R. Bach
Learning with Differentiable Perturbed Optimizers. CoRR (2020)
Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis R. Bach
Learning with Differentiable Pertubed Optimizers. NeurIPS (2020)
Mathieu Blondel, Arthur Mensch, Jean-Philippe Vert
Differentiable Divergences Between Time Series. CoRR (2020)
Arthur Mensch, Mathieu Blondel, Gabriel Peyré
Geometric Losses for Distributional Learning. CoRR (2019)
Mathieu Blondel
Structured Prediction with Projection Oracles. NeurIPS (2019)
Mathieu Blondel, André F. T. Martins, Vlad Niculae
Learning with Fenchel-Young Losses. CoRR (2019)
Mathieu Blondel
Structured Prediction with Projection Oracles. CoRR (2019)
Mathieu Blondel, André F. T. Martins, Vlad Niculae
Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms. AISTATS (2019)
Arthur Mensch, Mathieu Blondel, Gabriel Peyré
Geometric Losses for Distributional Learning. ICML (2019)
Vlad Niculae, André F. T. Martins, Mathieu Blondel, Claire Cardie
SparseMAP: Differentiable Sparse Structured Inference. ICML (2018)
Antoine Rolet, Vivien Seguy, Mathieu Blondel, Hiroshi Sawada
Blind Source Separation with Optimal Transport Non-negative Matrix Factorization. CoRR (2018)
Mathieu Blondel, Vivien Seguy, Antoine Rolet
Smooth and Sparse Optimal Transport. AISTATS (2018)
Arthur Mensch, Mathieu Blondel
Differentiable Dynamic Programming for Structured Prediction and Attention. CoRR (2018)
Vlad Niculae, André F. T. Martins, Mathieu Blondel, Claire Cardie
SparseMAP: Differentiable Sparse Structured Inference. CoRR (2018)
Arthur Mensch, Mathieu Blondel
Differentiable Dynamic Programming for Structured Prediction and Attention. ICML (2018)
Mathieu Blondel, André F. T. Martins, Vlad Niculae
Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms. CoRR (2018)
Antoine Rolet, Vivien Seguy, Mathieu Blondel, Hiroshi Sawada
Blind source separation with optimal transport non-negative matrix factorization. EURASIP J. Adv. Signal Process. 2018 (2018)
Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel
Large Scale Optimal Transport and Mapping Estimation. ICLR (Poster) (2018)
Mathieu Blondel, Vivien Seguy, Antoine Rolet
Smooth and Sparse Optimal Transport. CoRR (2017)
Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda
SVD-Based Screening for the Graphical Lasso. IJCAI (2017)
Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda
Multi-output Polynomial Networks and Factorization Machines. CoRR (2017)
Marco Cuturi, Mathieu Blondel
Soft-DTW: a Differentiable Loss Function for Time-Series. ICML (2017)
Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda
Multi-output Polynomial Networks and Factorization Machines. NIPS (2017)
Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda
Scaling Locally Linear Embedding. SIGMOD Conference (2017)
Vlad Niculae, Mathieu Blondel
A Regularized Framework for Sparse and Structured Neural Attention. NIPS (2017)
Vlad Niculae, Mathieu Blondel
A Regularized Framework for Sparse and Structured Neural Attention. CoRR (2017)
Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata
Higher-Order Factorization Machines. CoRR (2016)
Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata
Higher-Order Factorization Machines. NIPS (2016)
Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms. ICML (2016)
Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms. CoRR (2016)
Yukino Baba, Hisashi Kashima, Yasunobu Nohara, Eiko Kai, Partha Pratim Ghosh, Rafiqul Islam Maruf, Ashir Ahmed, Masahiro Kuroda, Sozo Inoue, Tatsuo Hiramatsu, Michio Kimura, Shuji Shimizu, Kunihisa Kobayashi, Koji Tsuda, Masashi Sugiyama, Mathieu Blondel, Naonori Ueda, Masaru Kitsuregawa, Naoki Nakashima
Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries. KDD (2015)
Mathieu Blondel, Akinori Fujino, Naonori Ueda
Convex Factorization Machines. ECML/PKDD (2) (2015)
Mathieu Blondel, Akinori Fujino, Naonori Ueda
Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex. ICPR (2014)
Mathieu Blondel, Yotaro Kubo, Naonori Ueda
Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion. AISTATS (2014)
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
API design for machine learning software: experiences from the scikit-learn project. CoRR (2013)
Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara
Learning non-linear classifiers with a sparsity constraint using L1 regularization. SAC (2013)
Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara
Block coordinate descent algorithms for large-scale sparse multiclass classification. Mach. Learn. 93 (1) (2013)
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake VanderPlas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Edouard Duchesnay
Scikit-learn: Machine Learning in Python CoRR (2012)
Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara
Tackling class imbalance and data scarcity in literature-based gene function annotation. SIGIR (2011)
Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara
Application of Semantic Kernels to Literature-Based Gene Function Annotation. Discovery Science (2011)
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake VanderPlas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Edouard Duchesnay
Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12 (2011)
Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara
Unsupervised Learning of Stroke Tagger for Online Kanji Handwriting Recognition. ICPR (2010)