Blondel, Mathieu

40 publications

AISTATS 2025 Implicit Diffusion: Efficient Optimization Through Stochastic Sampling Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet

ICML 2025 Joint Learning of Energy-Based Models and Their Partition Function Michael Eli Sander, Vincent Roulet, Tianlin Liu, Mathieu Blondel

ICML 2025 Loss Functions and Operators Generated by F-Divergences Vincent Roulet, Tianlin Liu, Nino Vieillard, Michael Eli Sander, Mathieu Blondel

ICML 2025 On Teacher Hacking in Language Model Distillation Daniil Tiapkin, Daniele Calandriello, Johan Ferret, Sarah Perrin, Nino Vieillard, Alexandre Rame, Mathieu Blondel

ICML 2024 Decoding-Time Realignment of Language Models Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares-López, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel

ICML 2024 How Do Transformers Perform In-Context Autoregressive Learning ? Michael Eli Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré

ICMLW 2024 Implicit Diffusion: Efficient Optimization Through Stochastic Sampling Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet

NeurIPS 2024 Learning with Fitzpatrick Losses Seta Rakotomandimby, Jean-Philippe Chancelier, Michel De Lara, Mathieu Blondel

TMLR 2024 Routers in Vision Mixture of Experts: An Empirical Study Tianlin Liu, Mathieu Blondel, Carlos Riquelme Ruiz, Joan Puigcerver

NeurIPS 2024 Stepping on the Edge: Curvature Aware Learning Rate Tuners Vincent Roulet, Atish Agarwala, Jean-Bastien Grill, Grzegorz Swirszcz, Mathieu Blondel, Fabian Pedregosa

ICML 2023 Fast, Differentiable and Sparse Top-K: A Convex Analysis Perspective Michael Eli Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel

ICLR 2023 Sparsity-Constrained Optimal Transport Tianlin Liu, Joan Puigcerver, Mathieu Blondel

AISTATS 2022 Sinkformers: Transformers with Doubly Stochastic Attention Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré

NeurIPS 2022 Efficient and Modular Implicit Differentiation Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-Lopez, Fabian Pedregosa, Jean-Philippe Vert

JMLR 2022 Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon

NeurIPS 2022 Learning Energy Networks with Generalized Fenchel-Young Losses Mathieu Blondel, Felipe Llinares-Lopez, Robert Dadashi, Leonard Hussenot, Matthieu Geist

JMLR 2022 Sparse Continuous Distributions and Fenchel-Young Losses André F. T. Martins, Marcos Treviso, António Farinhas, Pedro M. Q. Aguiar, Mário A. T. Figueiredo, Mathieu Blondel, Vlad Niculae

AISTATS 2021 Differentiable Divergences Between Time Series Mathieu Blondel, Arthur Mensch, Jean-Philippe Vert

ICML 2021 Momentum Residual Neural Networks Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré

ICML 2020 Fast Differentiable Sorting and Ranking Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga

ICML 2020 Implicit Differentiation of Lasso-Type Models for Hyperparameter Optimization Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon

NeurIPS 2020 Learning with Differentiable Pertubed Optimizers Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis R. Bach

JMLR 2020 Learning with Fenchel-Young Losses Mathieu Blondel, André F.T. Martins, Vlad Niculae

ICML 2019 Geometric Losses for Distributional Learning Arthur Mensch, Mathieu Blondel, Gabriel Peyré

AISTATS 2019 Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms Mathieu Blondel, Andre Martins, Vlad Niculae

NeurIPS 2019 Structured Prediction with Projection Oracles Mathieu Blondel

ICML 2018 Differentiable Dynamic Programming for Structured Prediction and Attention Arthur Mensch, Mathieu Blondel

ICLR 2018 Large Scale Optimal Transport and Mapping Estimation Vivien Seguy, Bharath Bhushan Damodaran, Remi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel

AISTATS 2018 Smooth and Sparse Optimal Transport Mathieu Blondel, Vivien Seguy, Antoine Rolet

ICML 2018 SparseMAP: Differentiable Sparse Structured Inference Vlad Niculae, Andre Martins, Mathieu Blondel, Claire Cardie

NeurIPS 2017 A Regularized Framework for Sparse and Structured Neural Attention Vlad Niculae, Mathieu Blondel

NeurIPS 2017 Multi-Output Polynomial Networks and Factorization Machines Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda

IJCAI 2017 SVD-Based Screening for the Graphical Lasso Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda

ICML 2017 Soft-DTW: A Differentiable Loss Function for Time-Series Marco Cuturi, Mathieu Blondel

NeurIPS 2016 Higher-Order Factorization Machines Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata

ICML 2016 Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda

ECML-PKDD 2015 Convex Factorization Machines Mathieu Blondel, Akinori Fujino, Naonori Ueda

AISTATS 2014 Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion Mathieu Blondel, Yotaro Kubo, Naonori Ueda

MLJ 2013 Block Coordinate Descent Algorithms for Large-Scale Sparse Multiclass Classification Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara

MLOSS 2011 Scikit-Learn: Machine Learning in Python 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, Édouard Duchesnay