Mathieu Blondel
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
30
Venues
10
Active years
2010–2025
Best venue rank
A*
Where they publish
Papers
30 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | AISTATS | Implicit Diffusion: Efficient optimization through stochastic sampling. | Pierre Marion, Anna Korba, Peter L. Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-Lpez, Courtney Paquette, Quentin Berthet |
| 2025 | ICML | Loss Functions and Operators Generated by f-Divergences. | Vincent Roulet, Tianlin Liu, Nino Vieillard, Michael Eli Sander, Mathieu Blondel |
| 2025 | ICML | Joint Learning of Energy-based Models and their Partition Function. | Michael Eli Sander, Vincent Roulet, Tianlin Liu, Mathieu Blondel |
| 2025 | ICML | On Teacher Hacking in Language Model Distillation. | Daniil Tiapkin, Daniele Calandriello, Johan Ferret, Sarah Perrin, Nino Vieillard, Alexandre Ram, Mathieu Blondel |
| 2024 | ICML | Decoding-time Realignment of Language Models. | Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares-Lpez, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel |
| 2024 | ICML | How do Transformers Perform In-Context Autoregressive Learning ? | Michael Eli Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyr |
| 2023 | ICLR | Sparsity-Constrained Optimal Transport. | Tianlin Liu, Joan Puigcerver, Mathieu Blondel |
| 2023 | ICML | Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. | Michael Eli Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyr, Mathieu Blondel |
| 2022 | AISTATS | Sinkformers: Transformers with Doubly Stochastic Attention. | Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyr |
| 2021 | AISTATS | Differentiable Divergences Between Time Series. | Mathieu Blondel, Arthur Mensch, Jean-Philippe Vert |
| 2021 | ICML | Momentum Residual Neural Networks. | Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyr |
| 2020 | ICML | Implicit differentiation of Lasso-type models for hyperparameter optimization. | Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon |
| 2020 | ICML | Fast Differentiable Sorting and Ranking. | Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga |
| 2019 | AISTATS | Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms. | Mathieu Blondel, Andr F. T. Martins, Vlad Niculae |
| 2019 | ICML | Geometric Losses for Distributional Learning. | Arthur Mensch, Mathieu Blondel, Gabriel Peyr |
| 2018 | AISTATS | Smooth and Sparse Optimal Transport. | Mathieu Blondel, Vivien Seguy, Antoine Rolet |
| 2018 | ICLR | Large Scale Optimal Transport and Mapping Estimation. | Vivien Seguy, Bharath Bhushan Damodaran, Rmi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel |
| 2018 | ICML | Differentiable Dynamic Programming for Structured Prediction and Attention. | Arthur Mensch, Mathieu Blondel |
| 2018 | ICML | SparseMAP: Differentiable Sparse Structured Inference. | Vlad Niculae, Andr F. T. Martins, Mathieu Blondel, Claire Cardie |
| 2017 | ICML | Soft-DTW: a Differentiable Loss Function for Time-Series. | Marco Cuturi, Mathieu Blondel |
| 2017 | IJCAI | SVD-Based Screening for the Graphical Lasso. | Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda |
| 2017 | SIGMOD | Scaling Locally Linear Embedding. | Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda |
| 2016 | ICML | Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms. | Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda |
| 2015 | KDD | 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 |
| 2014 | AISTATS | Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion. | Mathieu Blondel, Yotaro Kubo, Naonori Ueda |
| 2014 | ICPR | Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex. | Mathieu Blondel, Akinori Fujino, Naonori Ueda |
| 2013 | SAC | Learning non-linear classifiers with a sparsity constraint using L1 regularization. | Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara |
| 2011 | DIS | Application of Semantic Kernels to Literature-Based Gene Function Annotation. | Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara |
| 2011 | SIGIR | Tackling class imbalance and data scarcity in literature-based gene function annotation. | Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara |
| 2010 | ICPR | Unsupervised Learning of Stroke Tagger for Online Kanji Handwriting Recognition. | Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara |