Mathieu Blondel

发表

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

Naonori Ueda, Akinori Fujino, Mathieu Blondel, 2014, 2014 22nd International Conference on Pattern Recognition.

Learning with Differentiable Perturbed Optimizers pdf

Francis Bach, Jean-Philippe Vert, Marco Cuturi, 2020, ArXiv.

Sparse and Kernel Methods for Practical Machine Learning Applications(Machine Learning and Data Mining, Doctorial Theses on Aritificial Intelligence)

Mathieu Blondel, 2013 .

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

Kazuhiro Seki, Kuniaki Uehara, Mathieu Blondel, 2013, SAC '13.

Differentiable Dynamic Programming for Structured Prediction and Attention

Arthur Mensch, Mathieu Blondel, Mathieu Blondel, 2018, ICML.

Implicit differentiation of Lasso-type models for hyperparameter optimization pdf

Alexandre Gramfort, Joseph Salmon, Mathieu Blondel, 2020, ICML.

scikit-learn: Just a test

Manoj Kumar, Gilles Louppe, Jake Vanderplas, 2015 .

Structured Prediction with Projection Oracles

Mathieu Blondel, Mathieu Blondel, 2019, NeurIPS.

Application of Semantic Kernels to Literature-Based Gene Function Annotation

Kazuhiro Seki, Kuniaki Uehara, Mathieu Blondel, 2011, Discovery Science.

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

Masashi Sugiyama, Hisashi Kashima, Naonori Ueda, 2015, KDD.

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

Kazuhiro Seki, Kuniaki Uehara, Mathieu Blondel, 2011, SIGIR '11.

Blind source separation with optimal transport non-negative matrix factorization

Hiroshi Sawada, Vivien Seguy, Mathieu Blondel, 2018, EURASIP Journal on Advances in Signal Processing.

Independent consultant pdf

Gilles Louppe, Gaël Varoquaux, Vlad Niculae, 2013 .

Geometric Losses for Distributional Learning

Gabriel Peyré, Arthur Mensch, Mathieu Blondel, 2019, ICML.

Unsupervised Learning of Stroke Tagger for Online Kanji Handwriting Recognition

Kazuhiro Seki, Kuniaki Uehara, Mathieu Blondel, 2010, 2010 20th International Conference on Pattern Recognition.

Multi-output Polynomial Networks and Factorization Machines

Naonori Ueda, Takuma Otsuka, Vlad Niculae, 2017, NIPS.

Convex Factorization Machines

Naonori Ueda, Akinori Fujino, Mathieu Blondel, 2015, ECML/PKDD.

Differentiable Divergences Between Time Series pdf

Arthur Mensch, Jean-Philippe Vert, Mathieu Blondel, 2020, ArXiv.

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

André F. T. Martins, Vlad Niculae, Mathieu Blondel, 2018, AISTATS.

Scikit-learn: Machine Learning in Python

Gaël Varoquaux, Bertrand Thirion, Ron Weiss, 2011, J. Mach. Learn. Res..

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

Marco Cuturi, Mathieu Blondel, Marco Cuturi, 2017, ICML.

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

Naonori Ueda, Masakazu Ishihata, Akinori Fujino, 2016, ICML.

Block coordinate descent algorithms for large-scale sparse multiclass classification

Kazuhiro Seki, Kuniaki Uehara, Mathieu Blondel, 2013, Machine Learning.

Higher-Order Factorization Machines

Naonori Ueda, Masakazu Ishihata, Akinori Fujino, 2016, NIPS.

SparseMAP: Differentiable Sparse Structured Inference

Claire Cardie, André F. T. Martins, Vlad Niculae, 2018, ICML.

Large Scale Optimal Transport and Mapping Estimation

Nicolas Courty, Vivien Seguy, Rémi Flamary, 2017, ICLR.

Learning with Differentiable Pertubed Optimizers

Francis R. Bach, Jean-Philippe Vert, Marco Cuturi, 2020, NeurIPS.

Learning with Fenchel-Young Losses pdf

André F. T. Martins, Vlad Niculae, Mathieu Blondel, 2020, J. Mach. Learn. Res..

Smooth and Sparse Optimal Transport pdf

Vivien Seguy, Mathieu Blondel, Antoine Rolet, 2017, AISTATS.

A Regularized Framework for Sparse and Structured Neural Attention

Vlad Niculae, Mathieu Blondel, Mathieu Blondel, 2017, NIPS.

Scaling Locally Linear Embedding

Koh Takeuchi, Naonori Ueda, Tomoharu Iwata, 2017, SIGMOD Conference.

Fast Differentiable Sorting and Ranking

Olivier Teboul, Mathieu Blondel, Quentin Berthet, 2020, ICML.

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

Naonori Ueda, Yotaro Kubo, Mathieu Blondel, 2014, AISTATS.

SVD-Based Screening for the Graphical Lasso

Koh Takeuchi, Naonori Ueda, Tomoharu Iwata, 2017, IJCAI.

Momentum Residual Neural Networks pdf

Mathieu Blondel, Pierre Ablin, Michael E. Sander, 2021, ICML.

Efficient and Modular Implicit Differentiation pdf

Stephan Hoyer, Jean-Philippe Vert, Marco Cuturi, 2021, ArXiv.

Implicit differentiation for fast hyperparameter selection in non-smooth convex learning pdf

Alexandre Gramfort, Mathurin Massias, Joseph Salmon, 2021, ArXiv.

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

Neil Zeghidour, Olivier Teboul, Mathieu Blondel, 2021, IEEE Signal Processing Letters.

Sinkformers: Transformers with Doubly Stochastic Attention pdf

Mathieu Blondel, Pierre Ablin, Michael E. Sander, 2021, AISTATS.

Sparse Continuous Distributions and Fenchel-Young Losses pdf

Pedro M. Q. Aguiar, Vlad Niculae, Mathieu Blondel, 2021, ArXiv.

Health Checkup and Telemedical Intervention Program for Preventive Medicine in Developing Countries: Verification Study

Masashi Sugiyama, Hisashi Kashima, Naonori Ueda, 2015, Journal of medical Internet research.

Non-negative Matrix Factorization

Mikkel N. Schmidt, Kristoffer Hougaard Madsen, Mathieu Blondel, 2020, Definitions.

A Ranking Approach to Genomic Selection

Naonori Ueda, Mathieu Blondel, Hiroyoshi Iwata, 2015, PloS one.