Mathieu Blondel

According to our database, Mathieu Blondel authored at least 49 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

The Elements of Differentiable Programming.

Mathieu Blondel, Vincent Roulet

CoRR, 2024

How do Transformers perform In-Context Autoregressive Learning?

Michael E. Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré

CoRR, 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

CoRR, 2024

Direct Language Model Alignment from Online AI Feedback.

Shangmin Guo, Biao Zhang, Tianlin Liu, Tianqi Liu, Misha Khalman, Felipe Llinares, Alexandre Ramé, Thomas Mesnard, Yao Zhao, Bilal Piot, Johan Ferret, Mathieu Blondel

CoRR, 2024

Decoding-time Realignment of Language Models.

Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel

CoRR, 2024

Routers in Vision Mixture of Experts: An Empirical Study.

Tianlin Liu, Mathieu Blondel, Carlos Riquelme, Joan Puigcerver

CoRR, 2024

2023

Dual Gauss-Newton Directions for Deep Learning.

Vincent Roulet, Mathieu Blondel

CoRR, 2023

Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective.

Michael Eli Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel

Proceedings of the International Conference on Machine Learning, 2023

Sparsity-Constrained Optimal Transport.

Tianlin Liu, Joan Puigcerver, Mathieu Blondel

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Sparse Continuous Distributions and Fenchel-Young Losses.

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

J. Mach. Learn. Res., 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

J. Mach. Learn. Res., 2022

Cutting Some Slack for SGD with Adaptive Polyak Stepsizes.

Robert M. Gower, Mathieu Blondel, Nidham Gazagnadou, Fabian Pedregosa

CoRR, 2022

Learning Energy Networks with Generalized Fenchel-Young Losses.

Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Efficient and Modular Implicit Differentiation.

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sinkformers: Transformers with Doubly Stochastic Attention.

Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Self-Supervised Learning of Audio Representations From Permutations With Differentiable Ranking.

Andrew N. Carr, Quentin Berthet, Mathieu Blondel, Olivier Teboul, Neil Zeghidour

IEEE Signal Process. Lett., 2021

Momentum Residual Neural Networks.

Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré

Proceedings of the 38th International Conference on Machine Learning, 2021

Differentiable Divergences Between Time Series.

Mathieu Blondel, Arthur Mensch, Jean-Philippe Vert

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Learning with Fenchel-Young losses.

Mathieu Blondel, André F. T. Martins, Vlad Niculae

J. Mach. Learn. Res., 2020

Learning with Differentiable Perturbed Optimizers.

Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis R. Bach

CoRR, 2020

Learning with Differentiable Pertubed Optimizers.

Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis R. Bach

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Fast Differentiable Sorting and Ranking.

Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga

Proceedings of the 37th International Conference on Machine Learning, 2020

Implicit differentiation of Lasso-type models for hyperparameter optimization.

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

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Structured Prediction with Projection Oracles.

Mathieu Blondel

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Geometric Losses for Distributional Learning.

Arthur Mensch, Mathieu Blondel, Gabriel Peyré

Proceedings of the 36th International Conference on Machine Learning, 2019

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

Mathieu Blondel, André F. T. Martins, Vlad Niculae

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Blind source separation with optimal transport non-negative matrix factorization.

Antoine Rolet, Vivien Seguy, Mathieu Blondel, Hiroshi Sawada

EURASIP J. Adv. Signal Process., 2018

SparseMAP: Differentiable Sparse Structured Inference.

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

Proceedings of the 35th International Conference on Machine Learning, 2018

Differentiable Dynamic Programming for Structured Prediction and Attention.

Arthur Mensch, Mathieu Blondel

Proceedings of the 35th International Conference on Machine Learning, 2018

Large Scale Optimal Transport and Mapping Estimation.

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

Proceedings of the 6th International Conference on Learning Representations, 2018

Smooth and Sparse Optimal Transport.

Mathieu Blondel, Vivien Seguy, Antoine Rolet

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Scaling Locally Linear Embedding.

Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

A Regularized Framework for Sparse and Structured Neural Attention.

Vlad Niculae, Mathieu Blondel

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Multi-output Polynomial Networks and Factorization Machines.

Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

SVD-Based Screening for the Graphical Lasso.

Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

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

Marco Cuturi, Mathieu Blondel

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Higher-Order Factorization Machines.

Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

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

Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Convex Factorization Machines.

Mathieu Blondel, Akinori Fujino, Naonori Ueda

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries.

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

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

2014

Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex.

Mathieu Blondel, Akinori Fujino, Naonori Ueda

Proceedings of the 22nd International Conference on Pattern Recognition, 2014

Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion.

Mathieu Blondel, Yotaro Kubo, Naonori Ueda

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Block coordinate descent algorithms for large-scale sparse multiclass classification.

Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara

Mach. Learn., 2013

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

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

CoRR, 2013

Learning non-linear classifiers with a sparsity constraint using L1 regularization.

Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara

Proceedings of the 28th Annual ACM Symposium on Applied Computing, 2013

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, Edouard Duchesnay

J. Mach. Learn. Res., 2011

Tackling class imbalance and data scarcity in literature-based gene function annotation.

Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara

Proceedings of the Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2011

Application of Semantic Kernels to Literature-Based Gene Function Annotation.

Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara

Proceedings of the Discovery Science - 14th International Conference, 2011

2010

Unsupervised Learning of Stroke Tagger for Online Kanji Handwriting Recognition.

Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara

Proceedings of the 20th International Conference on Pattern Recognition, 2010