Mathieu Blondel

91 papers A* 24A 6B 3Misc 1Journal 51Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Differentiable Knapsack and Top-k Operators via Dynamic Programming. CoRR Germain Vivier-Ardisson, Michael Eli Sander, Axel Parmentier, Mathieu Blondel
2025	J	`jnl`	Autoregressive Language Models are Secretly Energy-Based Models: Insights into the Lookahead Capabilities of Next-Token Prediction. CoRR Mathieu Blondel, Michael E. Sander, Germain Vivier-Ardisson, Tianlin Liu, Vincent Roulet
2025	A	`conf`	Implicit Diffusion: Efficient optimization through stochastic sampling. AISTATS Pierre Marion, Anna Korba, Peter L. Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet
2025	A*	`conf`	Joint Learning of Energy-based Models and their Partition Function. ICML Michael Eli Sander, Vincent Roulet, Tianlin Liu, Mathieu Blondel
2025	J	`jnl`	Joint Learning of Energy-based Models and their Partition Function. CoRR Michael E. Sander, Vincent Roulet, Tianlin Liu, Mathieu Blondel
2025	J	`jnl`	Learning with Local Search MCMC Layers. CoRR Germain Vivier-Ardisson, Mathieu Blondel, Axel Parmentier
2025	A*	`conf`	Loss Functions and Operators Generated by f-Divergences. ICML Vincent Roulet, Tianlin Liu, Nino Vieillard, Michael Eli Sander, Mathieu Blondel
2025	J	`jnl`	Loss Functions and Operators Generated by f-Divergences. CoRR Vincent Roulet, Tianlin Liu, Nino Vieillard, Michael E. Sander, Mathieu Blondel
2025	A*	`conf`	On Teacher Hacking in Language Model Distillation. ICML Daniil Tiapkin, Daniele Calandriello, Johan Ferret, Sarah Perrin, Nino Vieillard, Alexandre Ramé, Mathieu Blondel
2025	J	`jnl`	On Teacher Hacking in Language Model Distillation. CoRR Daniil Tiapkin, Daniele Calandriello, Johan Ferret, Sarah Perrin, Nino Vieillard, Alexandre Ramé, Mathieu Blondel
2024	A*	`conf`	Decoding-time Realignment of Language Models. ICML Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares-López, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel
2024	J	`jnl`	Decoding-time Realignment of Language Models. CoRR Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel
2024	J	`jnl`	Direct Language Model Alignment from Online AI Feedback. CoRR Shangmin Guo, Biao Zhang, Tianlin Liu, Tianqi Liu, Misha Khalman, Felipe Llinares, Alexandre Ramé, Thomas Mesnard, Yao Zhao, Bilal Piot, Johan Ferret, Mathieu Blondel
2024	A*	`conf`	How do Transformers Perform In-Context Autoregressive Learning ? ICML Michael Eli Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré
2024	J	`jnl`	How do Transformers perform In-Context Autoregressive Learning? CoRR Michael E. Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré
2024	J	`jnl`	Implicit Diffusion: Efficient Optimization through Stochastic Sampling. CoRR Pierre Marion, Anna Korba, Peter L. Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet
2024	A*	`conf`	Learning with Fitzpatrick Losses. NeurIPS Seta Rakotomandimby, Jean-Philippe Chancelier, Michel De Lara, Mathieu Blondel
2024	J	`jnl`	Learning with Fitzpatrick Losses. CoRR Seta Rakotomandimby, Jean-Philippe Chancelier, Michel De Lara, Mathieu Blondel
2024	J	`jnl`	Routers in Vision Mixture of Experts: An Empirical Study. CoRR Tianlin Liu, Mathieu Blondel, Carlos Riquelme, Joan Puigcerver
2024	J	`jnl`	Routers in Vision Mixture of Experts: An Empirical Study. Trans. Mach. Learn. Res. Tianlin Liu, Mathieu Blondel, Carlos Riquelme Ruiz, Joan Puigcerver
2024	A*	`conf`	Stepping on the Edge: Curvature Aware Learning Rate Tuners. NeurIPS Vincent Roulet, Atish Agarwala, Jean-Bastien Grill, Grzegorz Swirszcz, Mathieu Blondel, Fabian Pedregosa
2024	J	`jnl`	Stepping on the Edge: Curvature Aware Learning Rate Tuners. CoRR Vincent Roulet, Atish Agarwala, Jean-Bastien Grill, Grzegorz Swirszcz, Mathieu Blondel, Fabian Pedregosa
2024	J	`jnl`	The Elements of Differentiable Programming. CoRR Mathieu Blondel, Vincent Roulet
2023	J	`jnl`	Dual Gauss-Newton Directions for Deep Learning. CoRR Vincent Roulet, Mathieu Blondel
2023	A*	`conf`	Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. ICML Michael Eli Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel
2023	J	`jnl`	Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. CoRR Michael E. Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel
2023	A*	`conf`	Sparsity-Constrained Optimal Transport. ICLR Tianlin Liu, Joan Puigcerver, Mathieu Blondel
2022	J	`jnl`	Cutting Some Slack for SGD with Adaptive Polyak Stepsizes. CoRR Robert M. Gower, Mathieu Blondel, Nidham Gazagnadou, Fabian Pedregosa
2022	A*	`conf`	Efficient and Modular Implicit Differentiation. NeurIPS Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert
2022	J	`jnl`	Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning. J. Mach. Learn. Res. Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
2022	A*	`conf`	Learning Energy Networks with Generalized Fenchel-Young Losses. NeurIPS Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist
2022	J	`jnl`	Learning Energy Networks with Generalized Fenchel-Young Losses. CoRR Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist
2022	A	`conf`	Sinkformers: Transformers with Doubly Stochastic Attention. AISTATS Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré
2022	J	`jnl`	Sparse Continuous Distributions and Fenchel-Young Losses. J. Mach. Learn. Res. André F. T. Martins, Marcos V. Treviso, António Farinhas, Pedro M. Q. Aguiar, Mário A. T. Figueiredo, Mathieu Blondel, Vlad Niculae
2022	J	`jnl`	Sparsity-Constrained Optimal Transport. CoRR Tianlin Liu, Joan Puigcerver, Mathieu Blondel
2021	A	`conf`	Differentiable Divergences Between Time Series. AISTATS Mathieu Blondel, Arthur Mensch, Jean-Philippe Vert
2021	J	`jnl`	Efficient and Modular Implicit Differentiation. CoRR Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert
2021	J	`jnl`	Implicit differentiation for fast hyperparameter selection in non-smooth convex learning. CoRR Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
2021	A*	`conf`	Momentum Residual Neural Networks. ICML Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré
2021	J	`jnl`	Momentum Residual Neural Networks. CoRR Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré
2021	J	`jnl`	Self-Supervised Learning of Audio Representations From Permutations With Differentiable Ranking. IEEE Signal Process. Lett. Andrew N. Carr, Quentin Berthet, Mathieu Blondel, Olivier Teboul, Neil Zeghidour
2021	J	`jnl`	Self-Supervised Learning of Audio Representations from Permutations with Differentiable Ranking. CoRR Andrew N. Carr, Quentin Berthet, Mathieu Blondel, Olivier Teboul, Neil Zeghidour
2021	J	`jnl`	Sinkformers: Transformers with Doubly Stochastic Attention. CoRR Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré
2021	J	`jnl`	Sparse Continuous Distributions and Fenchel-Young Losses. CoRR André F. T. Martins, Marcos V. Treviso, António Farinhas, Pedro M. Q. Aguiar, Mário A. T. Figueiredo, Mathieu Blondel, Vlad Niculae
2020	J	`jnl`	Differentiable Divergences Between Time Series. CoRR Mathieu Blondel, Arthur Mensch, Jean-Philippe Vert
2020	A*	`conf`	Fast Differentiable Sorting and Ranking. ICML Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga
2020	J	`jnl`	Fast Differentiable Sorting and Ranking. CoRR Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga
2020	A*	`conf`	Implicit differentiation of Lasso-type models for hyperparameter optimization. ICML Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
2020	J	`jnl`	Implicit differentiation of Lasso-type models for hyperparameter optimization. CoRR Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
2020	A*	`conf`	Learning with Differentiable Pertubed Optimizers. NeurIPS Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis R. Bach
2020	J	`jnl`	Learning with Differentiable Perturbed Optimizers. CoRR Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis R. Bach
2020	J	`jnl`	Learning with Fenchel-Young losses. J. Mach. Learn. Res. Mathieu Blondel, André F. T. Martins, Vlad Niculae
2019	A*	`conf`	Geometric Losses for Distributional Learning. ICML Arthur Mensch, Mathieu Blondel, Gabriel Peyré
2019	J	`jnl`	Geometric Losses for Distributional Learning. CoRR Arthur Mensch, Mathieu Blondel, Gabriel Peyré
2019	A	`conf`	Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms. AISTATS Mathieu Blondel, André F. T. Martins, Vlad Niculae
2019	J	`jnl`	Learning with Fenchel-Young Losses. CoRR Mathieu Blondel, André F. T. Martins, Vlad Niculae
2019	A*	`conf`	Structured Prediction with Projection Oracles. NeurIPS Mathieu Blondel
2019	J	`jnl`	Structured Prediction with Projection Oracles. CoRR Mathieu Blondel
2018	J	`jnl`	Blind Source Separation with Optimal Transport Non-negative Matrix Factorization. CoRR Antoine Rolet, Vivien Seguy, Mathieu Blondel, Hiroshi Sawada
2018	J	`jnl`	Blind source separation with optimal transport non-negative matrix factorization. EURASIP J. Adv. Signal Process. Antoine Rolet, Vivien Seguy, Mathieu Blondel, Hiroshi Sawada
2018	A*	`conf`	Differentiable Dynamic Programming for Structured Prediction and Attention. ICML Arthur Mensch, Mathieu Blondel
2018	J	`jnl`	Differentiable Dynamic Programming for Structured Prediction and Attention. CoRR Arthur Mensch, Mathieu Blondel
2018	—	`conf`	Large Scale Optimal Transport and Mapping Estimation. ICLR (Poster) Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel
2018	J	`jnl`	Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms. CoRR Mathieu Blondel, André F. T. Martins, Vlad Niculae
2018	A	`conf`	Smooth and Sparse Optimal Transport. AISTATS Mathieu Blondel, Vivien Seguy, Antoine Rolet
2018	A*	`conf`	SparseMAP: Differentiable Sparse Structured Inference. ICML Vlad Niculae, André F. T. Martins, Mathieu Blondel, Claire Cardie
2018	J	`jnl`	SparseMAP: Differentiable Sparse Structured Inference. CoRR Vlad Niculae, André F. T. Martins, Mathieu Blondel, Claire Cardie
2017	—	`conf`	A Regularized Framework for Sparse and Structured Neural Attention. NIPS Vlad Niculae, Mathieu Blondel
2017	J	`jnl`	A Regularized Framework for Sparse and Structured Neural Attention. CoRR Vlad Niculae, Mathieu Blondel
2017	—	`conf`	Multi-output Polynomial Networks and Factorization Machines. NIPS Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda
2017	J	`jnl`	Multi-output Polynomial Networks and Factorization Machines. CoRR Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda
2017	A*	`conf`	SVD-Based Screening for the Graphical Lasso. IJCAI Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda
2017	—	`conf`	Scaling Locally Linear Embedding. SIGMOD Conference Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda
2017	J	`jnl`	Smooth and Sparse Optimal Transport. CoRR Mathieu Blondel, Vivien Seguy, Antoine Rolet
2017	A*	`conf`	Soft-DTW: a Differentiable Loss Function for Time-Series. ICML Marco Cuturi, Mathieu Blondel
2016	—	`conf`	Higher-Order Factorization Machines. NIPS Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata
2016	J	`jnl`	Higher-Order Factorization Machines. CoRR Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata
2016	A*	`conf`	Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms. ICML Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda
2016	J	`jnl`	Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms. CoRR Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda
2015	—	`conf`	Convex Factorization Machines. ECML/PKDD (2) Mathieu Blondel, Akinori Fujino, Naonori Ueda
2015	A*	`conf`	Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries. KDD 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
2014	B	`conf`	Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex. ICPR Mathieu Blondel, Akinori Fujino, Naonori Ueda
2014	A	`conf`	Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion. AISTATS Mathieu Blondel, Yotaro Kubo, Naonori Ueda
2013	J	`jnl`	API design for machine learning software: experiences from the scikit-learn project. CoRR 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
2013	J	`jnl`	Block coordinate descent algorithms for large-scale sparse multiclass classification. Mach. Learn. Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara
2013	Misc	`conf`	Learning non-linear classifiers with a sparsity constraint using L1 regularization. SAC Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara
2012	J	`jnl`	Scikit-learn: Machine Learning in Python CoRR 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
2011	B	`conf`	Application of Semantic Kernels to Literature-Based Gene Function Annotation. Discovery Science Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara
2011	J	`jnl`	Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 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
2011	A*	`conf`	Tackling class imbalance and data scarcity in literature-based gene function annotation. SIGIR Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara
2010	B	`conf`	Unsupervised Learning of Stroke Tagger for Online Kanji Handwriting Recognition. ICPR Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara