Unsupervised Learning of Stroke Tagger for Online Kanji Handwriting Recognition. - Mathieu Blondel; Kazuhiro Seki; Kuniaki Uehara (ICPR, 2010)

Application of Semantic Kernels to Literature-Based Gene Function Annotation. - Mathieu Blondel; Kazuhiro Seki; Kuniaki Uehara (Discovery Science, 2011)

Tackling class imbalance and data scarcity in literature-based gene function annotation. - Mathieu Blondel; Kazuhiro Seki; Kuniaki Uehara (SIGIR, 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; et al. (J. Mach. Learn. Res., 2011)

Learning non-linear classifiers with a sparsity constraint using L1 regularization. - Mathieu Blondel; Kazuhiro Seki; Kuniaki Uehara (SAC, 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; et al. (CoRR, 2013)

Block coordinate descent algorithms for large-scale sparse multiclass classification. - Mathieu Blondel; Kazuhiro Seki; Kuniaki Uehara (Mach. Learn., 2013)

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

Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex. - Mathieu Blondel; Akinori Fujino; Naonori Ueda (ICPR, 2014)

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; et al. (KDD, 2015)

Convex Factorization Machines. - Mathieu Blondel; Akinori Fujino; Naonori Ueda (ECML/PKDD (2), 2015)

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

Higher-Order Factorization Machines. - Mathieu Blondel; Akinori Fujino; Naonori Ueda; Masakazu Ishihata (NIPS, 2016)

Soft-DTW: a Differentiable Loss Function for Time-Series. - Marco Cuturi; Mathieu Blondel (ICML, 2017)

SVD-Based Screening for the Graphical Lasso. - Yasuhiro Fujiwara; Naoki Marumo; Mathieu Blondel; Koh Takeuchi 001; Hideaki Kim; Tomoharu Iwata; Naonori Ueda (IJCAI, 2017)

Multi-output Polynomial Networks and Factorization Machines. - Mathieu Blondel; Vlad Niculae; Takuma Otsuka; Naonori Ueda (NIPS, 2017)

A Regularized Framework for Sparse and Structured Neural Attention. - Vlad Niculae; Mathieu Blondel (NIPS, 2017)

Scaling Locally Linear Embedding. - Yasuhiro Fujiwara; Naoki Marumo; Mathieu Blondel; Koh Takeuchi 001; Hideaki Kim; Tomoharu Iwata; Naonori Ueda (SIGMOD Conference, 2017)

Smooth and Sparse Optimal Transport. - Mathieu Blondel; Vivien Seguy; Antoine Rolet (AISTATS, 2018)

Large Scale Optimal Transport and Mapping Estimation. - Vivien Seguy; Bharath Bhushan Damodaran; Rémi Flamary; Nicolas Courty; Antoine Rolet; Mathieu Blondel (ICLR (Poster), 2018)

Differentiable Dynamic Programming for Structured Prediction and Attention. - Arthur Mensch; Mathieu Blondel (ICML, 2018)

SparseMAP: Differentiable Sparse Structured Inference. - Vlad Niculae; André F. T. Martins; Mathieu Blondel; Claire Cardie (ICML, 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)

Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms. - Mathieu Blondel; André F. T. Martins; Vlad Niculae (AISTATS, 2019)

Geometric Losses for Distributional Learning. - Arthur Mensch; Mathieu Blondel; Gabriel Peyré (ICML, 2019)

Structured Prediction with Projection Oracles. - Mathieu Blondel (NeurIPS, 2019)

Implicit differentiation of Lasso-type models for hyperparameter optimization. - Quentin Bertrand; Quentin Klopfenstein; Mathieu Blondel; Samuel Vaiter; Alexandre Gramfort; Joseph Salmon (ICML, 2020)

Fast Differentiable Sorting and Ranking. - Mathieu Blondel; Olivier Teboul; Quentin Berthet; Josip Djolonga (ICML, 2020)

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

Learning with Differentiable Perturbed Optimizers. - Quentin Berthet; Mathieu Blondel; Olivier Teboul; Marco Cuturi; Jean-Philippe Vert; Francis R. Bach (CoRR, 2020)

Learning with Fenchel-Young losses. - Mathieu Blondel; André F. T. Martins; Vlad Niculae (J. Mach. Learn. Res., 2020)

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

Momentum Residual Neural Networks. - Michael E. Sander; Pierre Ablin; Mathieu Blondel; Gabriel Peyré (ICML, 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)

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

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

Learning Energy Networks with Generalized Fenchel-Young Losses. - Mathieu Blondel; Felipe Llinares-López; Robert Dadashi; Léonard Hussenot; Matthieu Geist (NeurIPS, 2022)

Cutting Some Slack for SGD with Adaptive Polyak Stepsizes. - Robert M. Gower; Mathieu Blondel; Nidham Gazagnadou; Fabian Pedregosa (CoRR, 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)

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)

Sparsity-Constrained Optimal Transport. - Tianlin Liu; Joan Puigcerver; Mathieu Blondel (ICLR, 2023)

Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. - Michael Eli Sander; Joan Puigcerver; Josip Djolonga; Gabriel Peyré; Mathieu Blondel (ICML, 2023)

Dual Gauss-Newton Directions for Deep Learning. - Vincent Roulet; Mathieu Blondel (CoRR, 2023)

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 (ICML, 2024)

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

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

Direct Language Model Alignment from Online AI Feedback. - Shangmin Guo; Biao Zhang; Tianlin Liu; Tianqi Liu 002; Misha Khalman; Felipe Llinares; Alexandre Ramé; Thomas Mesnard; Yao Zhao; Bilal Piot; Johan Ferret; Mathieu Blondel (CoRR, 2024)

The Elements of Differentiable Programming. - Mathieu Blondel; Vincent Roulet (CoRR, 2024)

Routers in Vision Mixture of Experts: An Empirical Study. - Tianlin Liu; Mathieu Blondel; Carlos Riquelme Ruiz; Joan Puigcerver (Trans. Mach. Learn. Res., 2024)

Implicit Diffusion: Efficient optimization through stochastic sampling. - Pierre Marion; Anna Korba; Peter L. Bartlett; Mathieu Blondel; Valentin De Bortoli; Arnaud Doucet; Felipe Llinares-López; Courtney Paquette; Quentin Berthet (AISTATS, 2025)

Joint Learning of Energy-based Models and their Partition Function. - Michael E. Sander; Vincent Roulet; Tianlin Liu; Mathieu Blondel (CoRR, 2025)

Loss Functions and Operators Generated by f-Divergences. - Vincent Roulet; Tianlin Liu; Nino Vieillard; Michael E. Sander; Mathieu Blondel (CoRR, 2025)

On Teacher Hacking in Language Model Distillation. - Daniil Tiapkin; Daniele Calandriello; Johan Ferret; Sarah Perrin; Nino Vieillard; Alexandre Ramé; Mathieu Blondel (CoRR, 2025)

Learning with Local Search MCMC Layers. - Germain Vivier-Ardisson; Mathieu Blondel; Axel Parmentier (CoRR, 2025)