Sparse Structured Methods for Machine Learning
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SIERRA Sparse Structured Methods for Machine Learning |
Related courses / tutorials :
July
2012: Computer
Vision and Machine Learning Summer School, Grenoble - Kernel
methods and sparse methods for computer vision
July 2011:
Computer Vision
and Machine Learning Summer School, Paris - Kernel
methods and sparse methods for computer vision
September
2010:
ECML/PKDD
Tutorial on Sparse methods for machine learning (Theory and
algorithms)
July 2010:
Computer Vision
and Machine Learning Summer School, Grenoble - Kernel
methods and sparse methods for computer vision
July 2010:
Signal processing summer
school, Peyresq - Sparse
method for machine learning
June 2010:
CVPR
Tutorial on Sparse Coding and Dictionary Learning for Image Analysis
- slides
of ML part
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)
January
2008: Workshop
RASMA, Franceville, Gabon - Introduction to kernel methods (slides in
French)
Related publications:
R. Jenatton, A. Gramfort, V.
Michel, G. Obozinski, E. Eger, F. Bach, B. Thirion. Multi-scale
Mining of fMRI data with Hierarchical Structured Sparsity. SIAM
Journal on Imaging Sciences, 2012, 5(3):835-856, 2012. [pdf]
F. Bach, S. Lacoste-Julien, G. Obozinski. On
the Equivalence between Herding and Conditional Gradient Algorithms.
Proceedings of the International
Conference on Machine Learning (ICML), 2012.
F. Bach, R. Jenatton, J. Mairal, G. Obozinski. Optimization
with sparsity-inducing penalties. Foundations
and Trends in Machine Learning, 4(1):1-106, 2012. [
J. Mairal, F. Bach, J. Ponce. Task-Driven
Dictionary
Learning. IEEE
Transactions on Pattern Analysis and Machine Intelligence,
34(4):791-804, 2012. [pdf
F. Bach.
C. Archambeau, F. Bach. Multiple
Gaussian process models. Technical Report Arxiv 110.5238, 2011. [pdf]
F. Bach, E. Moulines. Non-Asymptotic
Analysis of Stochastic Approximation Algorithms for Machine Learning.
Advances in Neural Information
Processing Systems (NIPS), 2011. [pdf]
[long-version-pdf-HAL]
M. Schmidt, N. Le Roux, F. Bach.
Convergence Rates of Inexact
Proximal-Gradient Methods for Convex Optimization. Advances
in Neural Information Processing Systems (NIPS), 2011. [pdf]
[
E. Grave, G. Obozinski, F. Bach.
B. Mishra, G. Meyer, F. Bach, R. Sepulchre. Low-rank
optimization with trace norm penalty. Technical report, Arxiv
1112.2318, 2011. [
R. Jenatton, J.-Y. Audibert and F. Bach. Structured
Variable Selection with Sparsity-inducing Norms. Journal of
Machine Learning Research, 12, 2777-2824, 2011. [pdf]
[code]
N. Le Roux, F. Bach. Local component
analysis. Technical report, HAL 00617965, 2011. [
J. Mairal, R. Jenatton, G. Obozinski, F. Bach. Convex
and Network Flow Optimization for Structured Sparsity.
R. Jenatton, J. Mairal, G. Obozinski, F. Bach. Proximal
Methods for Hierarchical Sparse Coding. Journal
of Machine Learning Research, 12,
2297-2334, 2011. [pdf]
F. Couzinie-Devy, J. Mairal, F. Bach and J. Ponce. Dictionary
Learning for Deblurring and Digital Zoom. Technical report, HAL :
inria- 00627402, 2011. [pdf]
Y-L. Boureau, N. Le Roux, F. Bach, J. Ponce, and Y. LeCun. Ask
the locals: multi-way local pooling for image recognition.
Proceedings of the International Conference on Computer Vision (ICCV),
2011. [pdf]
M. Solnon, S. Arlot, F. Bach. Multi-task
Regression using Minimal Penalties. Technical report, HAL
00610534, 2011. [pdf]
A. Lefèvre, F. Bach, C. Févotte. Online
algorithms for Nonnegative Matrix Factorization with the Itakura-Saito
divergence. Technical report, HAL 00602050, 2011. IEEE
Workshop on Applications of Signal Processing to Audio and Acoustics
(WASPAA), 2011. [pdf]
T. Hocking, A. Joulin, F. Bach and J.-P. Vert. Clusterpath:
an Algorithm for Clustering using Convex Fusion Penalties. Proceedings
of the International Conference on
Machine Learning (ICML), 2011. [pdf]
L. Benoît, J. Mairal, F. Bach, J. Ponce, Sparse
Image Representation with Epitomes. Proceedings
of the Conference on Computer Vision and Pattern Recognition (CVPR),
2011. [pdf]
A. Lefèvre, F. Bach, C. Févotte, Itakura-Saito
nonnegative matrix factorization with group sparsity, Proceedings
of the International Conference on Acoustics, Speech, and Signal
Processing (ICASSP), 2011. [pdf]
F. Bach, R. Jenatton, J. Mairal and G. Obozinski. Convex
optimization with sparsity-inducing norms. In S. Sra, S. Nowozin,
S. J. Wright., editors, Optimization for Machine Learning, MIT Press,
2011. [pdf]
2010
F. Bach. Convex Analysis and
Optimization with Submodular Functions: a Tutorial. Technical
report, HAL 00527714, 2010. [pdf]
F. Bach. Structured Sparsity-Inducing
Norms through Submodular Functions. Advances
in Neural Information Processing Systems (NIPS), 2010. [pdf]
[long version, arxiv] [slides]
J. Mairal, R. Jenatton, G. Obozinski, F. Bach. Network
Flow Algorithms for Structured Sparsity. Advances
in Neural Information Processing Systems (NIPS), 2010. [pdf]
F. Bach, S. D. Ahipasaoglu, A. d'Aspremont. Convex
Relaxations for Subset Selection. Technical report Arxiv
1006-3601. [pdf]
M. Hoffman, D. Blei, F. Bach. Online
Learning for Latent Dirichlet Allocation. Advances
in Neural Information Processing Systems (NIPS), 2010. [pdf]
F. Bach, S. D. Ahipasaoglu, A. d'Aspremont. Convex
Relaxations for Subset Selection. Technical report, ArXiv
1006.3601, 2010. [pdf]
R. Jenatton, J. Mairal, G. Obozinski, F. Bach. Proximal
Methods for Sparse Hierarchical Dictionary Learning. Proceedings
of the International Conference on
Machine Learning (ICML), 2010. [pdf]
[slides]
M. Journée, F. Bach, P.-A. Absil and R. Sepulchre. Low-Rank
Optimization on the Cone of Positive Semidefinite Matrices. SIAM
Journal on Optimization, 20(5):2327–2351, 2010. [pdf]
[code]
Y-L. Boureau, F. Bach, Y. LeCun, J. Ponce. Learning
Mid-Level Features For Recognition. Proceedings
of
the Conference on Computer Vision and Pattern Recognition (CVPR),
2010. [pdf]
R. Jenatton, G. Obozinski, F. Bach. Structured
Sparse
Principal Component Analysis. Proceedings of the
International Conference on Artificial Intelligence and Statistics
(AISTATS), 2010. [pdf]
[code]
Sparse modeling software - SPAM (C)
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)