Sparse Structured Methods for Machine Learning
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SIERRA Sparse Structured Methods for Machine Learning |
Related courses / tutorials :
Spring 2010: Statistical machine learning - Master M2 "Probabilites et Statistiques" - Universite Paris-Sud (Orsay)
December 2009: NIPS Tutorial on Sparse methods for machine learning (Theory and algorithms)
September 2009: ICCV Tutorial on Sparse Coding and Dictionary Learning for Image Analysis
September 2008: Machine Learning Summer School - Ile
de Re - Learning with sparsity inducing norms (slides)
October
2008: ECCV Tutorial on Supervised Learning: Introduction - Part I (Theory) - Part II (Algorithms)
Related publications:
2010
R. Jenatton, G. Obozinski, F. Bach. Structured Sparse Principal Component Analysis. International Conference on
Artificial Intelligence and Statistics (AISTATS), 2010. [pdf]
F. Bach. Self-Concordant Analysis for Logistic Regression. Technical report, HAL-00426227, 2009. To appear in Electronic Journal of Statistics. [pdf]
J. Mairal, F. Bach, J. Ponce, G. Sapiro. Online Learning for Matrix Factorization and Sparse Coding. Journal of Machine Learning Research, 11, 10-60, 2010. [pdf]
2009
F. Bach. High-Dimensional Non-Linear Variable Selection through Hierarchical Kernel Learning. Technical report, HAL 00413473, 2009. [pdf] [code]
J. Mairal, F. Bach, J. Ponce, G. Sapiro and A. Zisserman. Non-Local Sparse Models for Image Restoration. International Conference on Computer Vision (ICCV), 2009. [pdf]
R. Jenatton, J.-Y. Audibert and F. Bach. Structured variable selection with sparsity-inducing norms. Technical report, arXiv:0904.3523v1. [pdf]
J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online dictionary learning for sparse coding. International
Conference on Machine Learning (ICML), 2009. [pdf]
F. Bach, Model-consistent sparse estimation through the bootstrap, Technical report HAL-00354771, 2009. [pdf]
J. Abernethy, F. Bach,
T. Evgeniou, and J.-P. Vert, A
New Approach to Collaborative Filtering: Operator Estimation with
Spectral Regularization. Journal
of Machine Learning Research, 10:803-826, 2009 [pdf]
2008
F. Bach, J. Mairal, J. Ponce, Convex Sparse Matrix Factorizations, Technical report HAL-00345747, 2008. [pdf]
F. Bach. Exploring Large Feature Spaces
with Hierarchical Multiple Kernel Learning. Advances
in Neural Information Processing Systems (NIPS),
2008. [pdf] [HAL
tech-report] [matlab code]
J.
Mairal, F. Bach, J. Ponce, G. Sapiro and A. Zisserman. Supervised Dictionary Learning. Advances
in Neural Information Processing Systems (NIPS), 2008. [pdf]
L. Jacob, F.
Bach, J.-P. Vert. Clustered
Multi-Task Learning: A Convex Formulation. Advances
in Neural Information Processing Systems (NIPS),
2008. [pdf]
C. Archambeau, F. Bach. Sparse probabilistic projections, Advances in Neural Information Processing Systems (NIPS), 2008. [pdf]
A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet.
SimpleMKL. Journal
of Machine Learning Research, 9, 2491-2521, 2008. [pdf]
[code]
J. Mairal, M. Leordeanu, F. Bach, M. Hebert and J. Ponce. Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation. Proceedings of the European Conference on Computer Vision (ECCV), 2008. [pdf]
F.
Bach. Bolasso: model
consistent Lasso estimation through the bootstrap. Proceedings
of the Twenty-fifth International
Conference on Machine Learning (ICML), 2008.
[pdf] [slides]
A. d'Aspremont,
F. Bach and L. El Ghaoui. Optimal solutions for sparse
principal component analysis. Journal
of Machine Learning Research, 9, 1269-1294. [pdf]
[source
code] [slides]
J. Mairal, F. Bach, J. Ponce, G. Sapiro and A. Zisserman. Discriminative Learned Dictionaries for Local Image Analysis, Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), 2008. [pdf]
2007
A. d'Aspremont, F. Bach and L. El Ghaoui.
Full
regularization path for sparse principal component analysis.
Proceedings of the Twenty-fourth International
Conference on Machine Learning (ICML), 2007. [pdf] [tech-report, arXiv]
F. Bach, M. I. Jordan. Predictive
low-rank decomposition for kernel methods.
Proceedings of the Twenty-second International
Conference on Machine Learning (ICML), 2005. [pdf] [matlab/C code] [slides]
2004
F. Bach, R. Thibaux, M. I. Jordan. Computing regularization paths for learning
multiple kernels.. Advances
in Neural Information Processing Systems (NIPS) 17, 2004. [pdf] [matlab code] [slides]
F. Bach, G. R. G. Lanckriet, M. I. Jordan. Multiple Kernel Learning, Conic Duality, and the SMO Algorithm. Proceedings of the Twenty-first International Conference on Machine Learning, 2004 [pdf] [tech-report]
Related software:
Computing
regularization paths for multiple kernel learning - version 1.0 (matlab)
Predictive low-rank decomposition for kernel methods - version 1.0 (matlab/C)
Support Kernel Machine - Multiple kernel learning (matlab)
SimpleMKL - version 1.0 (matlab)
Grouplasso - version 1.0 (matlab)
Hierarchical kernel learning - version 3.0 (matlab)