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ORCIDMathieu Blondel
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2020 – today
- 2024
[j8]Tianlin Liu, Mathieu Blondel, Carlos Riquelme Ruiz, Joan Puigcerver:
Routers in Vision Mixture of Experts: An Empirical Study. Trans. Mach. Learn. Res. 2024 (2024)- [c34]Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares-López, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel:
Decoding-time Realignment of Language Models. ICML 2024 - [c33]Michael Eli Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré:
How do Transformers Perform In-Context Autoregressive Learning ? ICML 2024 - [i37]Tianlin Liu, Mathieu Blondel, Carlos Riquelme, Joan Puigcerver:
Routers in Vision Mixture of Experts: An Empirical Study. CoRR abs/2401.15969 (2024) - [i36]Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel:
Decoding-time Realignment of Language Models. CoRR abs/2402.02992 (2024) - [i35]Shangmin Guo, Biao Zhang, Tianlin Liu, Tianqi Liu, Misha Khalman, Felipe Llinares, Alexandre Ramé, Thomas Mesnard, Yao Zhao, Bilal Piot, Johan Ferret, Mathieu Blondel:
Direct Language Model Alignment from Online AI Feedback. CoRR abs/2402.04792 (2024) - [i34]Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet:
Implicit Diffusion: Efficient Optimization through Stochastic Sampling. CoRR abs/2402.05468 (2024) - [i33]Michael E. Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré:
How do Transformers perform In-Context Autoregressive Learning? CoRR abs/2402.05787 (2024) - [i32]Mathieu Blondel, Vincent Roulet:
The Elements of Differentiable Programming. CoRR abs/2403.14606 (2024) - [i31]Seta Rakotomandimby, Jean-Philippe Chancelier, Michel De Lara, Mathieu Blondel:
Learning with Fitzpatrick Losses. CoRR abs/2405.14574 (2024) - [i30]Vincent Roulet, Atish Agarwala, Jean-Bastien Grill, Grzegorz Swirszcz, Mathieu Blondel, Fabian Pedregosa:
Stepping on the Edge: Curvature Aware Learning Rate Tuners. CoRR abs/2407.06183 (2024) - 2023
- [c32]Tianlin Liu, Joan Puigcerver, Mathieu Blondel:
Sparsity-Constrained Optimal Transport. ICLR 2023 - [c31]Michael Eli Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel:
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. ICML 2023: 29919-29936 - [i29]Michael E. Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel:
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. CoRR abs/2302.01425 (2023) - [i28]Vincent Roulet, Mathieu Blondel:
Dual Gauss-Newton Directions for Deep Learning. CoRR abs/2308.08886 (2023) - 2022
- [j7]Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning. J. Mach. Learn. Res. 23: 149:1-149:43 (2022) - [j6]André F. T. Martins, Marcos V. Treviso, António Farinhas, Pedro M. Q. Aguiar, Mário A. T. Figueiredo, Mathieu Blondel, Vlad Niculae:
Sparse Continuous Distributions and Fenchel-Young Losses. J. Mach. Learn. Res. 23: 257:1-257:74 (2022) - [c30]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Sinkformers: Transformers with Doubly Stochastic Attention. AISTATS 2022: 3515-3530 - [c29]Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert:
Efficient and Modular Implicit Differentiation. NeurIPS 2022 - [c28]Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist:
Learning Energy Networks with Generalized Fenchel-Young Losses. NeurIPS 2022 - [i27]Robert M. Gower, Mathieu Blondel, Nidham Gazagnadou, Fabian Pedregosa:
Cutting Some Slack for SGD with Adaptive Polyak Stepsizes. CoRR abs/2202.12328 (2022) - [i26]Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist:
Learning Energy Networks with Generalized Fenchel-Young Losses. CoRR abs/2205.09589 (2022) - [i25]Tianlin Liu, Joan Puigcerver, Mathieu Blondel:
Sparsity-Constrained Optimal Transport. CoRR abs/2209.15466 (2022) - 2021
- [j5]Andrew N. Carr
, Quentin Berthet, Mathieu Blondel, Olivier Teboul, Neil Zeghidour:
Self-Supervised Learning of Audio Representations From Permutations With Differentiable Ranking. IEEE Signal Process. Lett. 28: 708-712 (2021) - [c27]Mathieu Blondel, Arthur Mensch, Jean-Philippe Vert:
Differentiable Divergences Between Time Series. AISTATS 2021: 3853-3861 - [c26]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Momentum Residual Neural Networks. ICML 2021: 9276-9287 - [i24]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Momentum Residual Neural Networks. CoRR abs/2102.07870 (2021) - [i23]Andrew N. Carr, Quentin Berthet, Mathieu Blondel, Olivier Teboul, Neil Zeghidour:
Self-Supervised Learning of Audio Representations from Permutations with Differentiable Ranking. CoRR abs/2103.09879 (2021) - [i22]Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning. CoRR abs/2105.01637 (2021) - [i21]Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert:
Efficient and Modular Implicit Differentiation. CoRR abs/2105.15183 (2021) - [i20]André F. T. Martins, Marcos V. Treviso, António Farinhas, Pedro M. Q. Aguiar, Mário A. T. Figueiredo, Mathieu Blondel, Vlad Niculae:
Sparse Continuous Distributions and Fenchel-Young Losses. CoRR abs/2108.01988 (2021) - [i19]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Sinkformers: Transformers with Doubly Stochastic Attention. CoRR abs/2110.11773 (2021) - 2020
- [j4]Mathieu Blondel, André F. T. Martins, Vlad Niculae:
Learning with Fenchel-Young losses. J. Mach. Learn. Res. 21: 35:1-35:69 (2020) - [c25]Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Implicit differentiation of Lasso-type models for hyperparameter optimization. ICML 2020: 810-821 - [c24]Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga:
Fast Differentiable Sorting and Ranking. ICML 2020: 950-959 - [c23]Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis R. Bach:
Learning with Differentiable Pertubed Optimizers. NeurIPS 2020 - [i18]Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis R. Bach:
Learning with Differentiable Perturbed Optimizers. CoRR abs/2002.08676 (2020) - [i17]Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga:
Fast Differentiable Sorting and Ranking. CoRR abs/2002.08871 (2020) - [i16]Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon:
Implicit differentiation of Lasso-type models for hyperparameter optimization. CoRR abs/2002.08943 (2020) - [i15]Mathieu Blondel, Arthur Mensch, Jean-Philippe Vert:
Differentiable Divergences Between Time Series. CoRR abs/2010.08354 (2020)
2010 – 2019
- 2019
- [c22]Mathieu Blondel, André F. T. Martins, Vlad Niculae:
Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms. AISTATS 2019: 606-615 - [c21]Arthur Mensch, Mathieu Blondel, Gabriel Peyré:
Geometric Losses for Distributional Learning. ICML 2019: 4516-4525 - [c20]Mathieu Blondel:
Structured Prediction with Projection Oracles. NeurIPS 2019: 12145-12156 - [i14]Mathieu Blondel, André F. T. Martins, Vlad Niculae:
Learning with Fenchel-Young Losses. CoRR abs/1901.02324 (2019) - [i13]Arthur Mensch, Mathieu Blondel, Gabriel Peyré:
Geometric Losses for Distributional Learning. CoRR abs/1905.06005 (2019) - [i12]Mathieu Blondel:
Structured Prediction with Projection Oracles. CoRR abs/1910.11369 (2019) - 2018
- [j3]Antoine Rolet, Vivien Seguy, Mathieu Blondel, Hiroshi Sawada:
Blind source separation with optimal transport non-negative matrix factorization. EURASIP J. Adv. Signal Process. 2018: 53 (2018) - [c19]Mathieu Blondel, Vivien Seguy, Antoine Rolet:
Smooth and Sparse Optimal Transport. AISTATS 2018: 880-889 - [c18]Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel:
Large Scale Optimal Transport and Mapping Estimation. ICLR (Poster) 2018 - [c17]Arthur Mensch, Mathieu Blondel:
Differentiable Dynamic Programming for Structured Prediction and Attention. ICML 2018: 3459-3468 - [c16]Vlad Niculae, André F. T. Martins, Mathieu Blondel, Claire Cardie:
SparseMAP: Differentiable Sparse Structured Inference. ICML 2018: 3796-3805 - [i11]Arthur Mensch, Mathieu Blondel:
Differentiable Dynamic Programming for Structured Prediction and Attention. CoRR abs/1802.03676 (2018) - [i10]Vlad Niculae, André F. T. Martins, Mathieu Blondel, Claire Cardie:
SparseMAP: Differentiable Sparse Structured Inference. CoRR abs/1802.04223 (2018) - [i9]Antoine Rolet, Vivien Seguy, Mathieu Blondel, Hiroshi Sawada:
Blind Source Separation with Optimal Transport Non-negative Matrix Factorization. CoRR abs/1802.05429 (2018) - [i8]Mathieu Blondel, André F. T. Martins, Vlad Niculae:
Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms. CoRR abs/1805.09717 (2018) - 2017
- [c15]Marco Cuturi, Mathieu Blondel:
Soft-DTW: a Differentiable Loss Function for Time-Series. ICML 2017: 894-903 - [c14]Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda:
SVD-Based Screening for the Graphical Lasso. IJCAI 2017: 1682-1688 - [c13]Vlad Niculae, Mathieu Blondel:
A Regularized Framework for Sparse and Structured Neural Attention. NIPS 2017: 3338-3348 - [c12]Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda:
Multi-output Polynomial Networks and Factorization Machines. NIPS 2017: 3349-3359 - [c11]Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda:
Scaling Locally Linear Embedding. SIGMOD Conference 2017: 1479-1492 - [i7]Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda:
Multi-output Polynomial Networks and Factorization Machines. CoRR abs/1705.07603 (2017) - [i6]Vlad Niculae, Mathieu Blondel:
A Regularized Framework for Sparse and Structured Neural Attention. CoRR abs/1705.07704 (2017) - [i5]Mathieu Blondel, Vivien Seguy, Antoine Rolet:
Smooth and Sparse Optimal Transport. CoRR abs/1710.06276 (2017) - 2016
- [c10]Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda:
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms. ICML 2016: 850-858 - [c9]Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata:
Higher-Order Factorization Machines. NIPS 2016: 3351-3359 - [i4]Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata:
Higher-Order Factorization Machines. CoRR abs/1607.07195 (2016) - [i3]Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda:
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms. CoRR abs/1607.08810 (2016) - 2015
- [c8]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:
Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries. KDD 2015: 1681-1690 - [c7]Mathieu Blondel, Akinori Fujino, Naonori Ueda:
Convex Factorization Machines. ECML/PKDD (2) 2015: 19-35 - 2014
- [c6]Mathieu Blondel, Yotaro Kubo, Naonori Ueda:
Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion. AISTATS 2014: 96-104 - [c5]Mathieu Blondel, Akinori Fujino, Naonori Ueda:
Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex. ICPR 2014: 1289-1294 - 2013
- [j2]Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara:
Block coordinate descent algorithms for large-scale sparse multiclass classification. Mach. Learn. 93(1): 31-52 (2013) - [c4]Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara:
Learning non-linear classifiers with a sparsity constraint using L1 regularization. SAC 2013: 167-169 - [i2]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:
API design for machine learning software: experiences from the scikit-learn project. CoRR abs/1309.0238 (2013) - 2012
- [i1]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:
Scikit-learn: Machine Learning in Python. CoRR abs/1201.0490 (2012) - 2011
- [j1]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:
Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12: 2825-2830 (2011) - [c3]Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara:
Application of Semantic Kernels to Literature-Based Gene Function Annotation. Discovery Science 2011: 61-75 - [c2]Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara:
Tackling class imbalance and data scarcity in literature-based gene function annotation. SIGIR 2011: 1123-1124 - 2010
- [c1]Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara:
Unsupervised Learning of Stroke Tagger for Online Kanji Handwriting Recognition. ICPR 2010: 1973-1976
Coauthor Index
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last updated on 2024-09-13 00:42 CEST by the dblp team
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