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Francis Bach INRIA
- Willow project CS 81321
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SIERRA: sparse structured methods for machine learning - ERC-funded project Now hiring PhD students and postdocs |
Courses / Tutorials :
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
Fall 2009: An introduction to
graphical models - Master M2 "Mathématiques,
Vision,
Apprentissage" - Ecole Normale Supérieure de Cachan
September 2008: Machine Learning Summer School - Ile
de Re - Learning with sparsity inducing norms (slides)
Fall 2008: An introduction to
graphical models - Master M2 "Mathématiques,
Vision,
Apprentissage" - Ecole Normale Supérieure de Cachan
October
2008: ECCV Tutorial on Supervised Learning: Introduction - Part I (Theory) - Part II (Algorithms)
May 2008: Probabilistic modelling and graphical models: Enseignement Spécialisé - Ecole des Mines de Paris
January 2008: Workshop RASMA, Franceville, Gabon -
Introduction to kernel methods (slides in French)
May
2007: Probabilistic
modelling and graphical models: Enseignement
Spécialisé - Ecole des Mines de Paris
Fall 2006: An introduction to graphical models
- Master M2 "Mathématiques,
Vision, Apprentissage" - Ecole Normale Supérieure de Cachan
Fall 2005: An introduction to graphical models
- Master M2 "Mathématiques,
Vision, Apprentissage" - Ecole Normale Supérieure de Cachan
SMILE: Statistical
Machine Learning in Paris : seminar / reading group
MGA: Projet ANR Modeles graphiques et applications
Publications:
2009
F. Bach. Self-Concordant Analysis for Logistic Regression. Technical report, HAL-00426227, 2009. [pdf]
R. Jenatton, G. Obozinski, F. Bach. Structured Sparse Principal Component Analysis. Technical report, HAL 00414158, 2009. [pdf]
P. Liang, F. Bach, G. Bouchard, M. I. Jordan. Asymptotically optimal regularization in smooth parametric models. Advances in Neural Information Processing Systems (NIPS), 2009. [pdf]
S. Arlot, F. Bach. Data-driven calibration of linear estimators with minimal penalties. Advances in Neural Information Processing Systems (NIPS), 2009. [techreport HAL 00414774 - pdf]
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. Online Learning for Matrix Factorization and Sparse Coding. Technical report, arXiv:0908.0050v1. To appear in Journal of Machine Learning Research, 2009. [pdf]
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]
O. Duchenne, I. Laptev, J. Sivic, F. Bach and J. Ponce. Automatic Annotation of Human Actions in Video. 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:0904.3523v2, 2009. [pdf]
J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online dictionary learning for sparse coding. International
Conference on Machine Learning (ICML), 2009. [pdf]
O. Duchenne, F. Bach, I. Kweon, and J. Ponce. A tensor-based algorithm for high-order graph matching. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009. [pdf]
M. Zaslavskiy, F. Bach and J.-P. Vert, Global alignment of protein-protein interaction networks by graph matching methods. Bioinformatics, 25(12):1259-1267, 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]
M. Zaslavskiy,
F. Bach and J.-P. Vert, A
path following algorithm for the graph matching problem. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12), 2227-2242, 2009. [pdf]
K. Fukumizu, F. Bach, and M. I. Jordan. Kernel dimension reduction in
regression. Annals
of Statistics, 37(4), 1871-1905, 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]
Z. Harchaoui, F. Bach, and E. Moulines. Kernel change-point analysis, 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]
M.
Journée, F. Bach, P.-A. Absil and R. Sepulchre. Low-rank optimization for
semidefinite convex problems. Tecnical report ArXiv
0807.4423v1, 2008. [pdf]
[code]
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]
F. Bach. Graph kernels between point clouds. Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML), 2008. [pdf]
2007
F. Bach and Z. Harchaoui. DIFFRAC
: a discriminative and flexible framework for clustering, Advances
in Neural Information Processing Systems (NIPS) 20, 2007.
[pdf] [slides]
A. M. Cord, D. Jeulin and F. Bach. Segmentation of random textures
by morphological and linear operators. Proceedings of
F. Bach, Active learning for misspecified generalized linear models, Advances in Neural Information Processing Systems (NIPS) 19, 2006. [pdf] [tech-report]
F. Bach, M. I. Jordan, Learning spectral
clustering, with application to speech separation, Journal
of
Machine Learning Research, 7, 1963-2001, 2006. [pdf] [speech
samples]
F. Bach, D. Heckerman, E. Horvitz, Considering cost asymmetry in learning classifiers, Journal of Machine Learning Research, 7, 1713-1741, 2006. [pdf]
K. Fukumizu, F. Bach, Arthur Gretton. Consistency of
Kernel Canonical Correlation Analysis. Advances
in Neural Information Processing Systems (NIPS) 18, 2005. [pdf]
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]
F. Bach, M. I. Jordan. A
probabilistic interpretation of canonical correlation analysis.
Technical Report 688, Department of Statistics, University of
California, Berkeley, 2005 [pdf]
F. Bach, D. Heckerman, E. Horvitz, On the
path to an ideal ROC Curve: considering cost asymmetry in learning
classifiers, Tenth International Workshop on
Artificial Intelligence and Statistics (AISTATS), 2005 [pdf] [pdf, technical report
MSR-TR-2004-24] [slides]
F. Bach, M. I. Jordan. Discriminative training of hidden
Markov models for multiple pitch tracking, Proceedings
of the International Conference on Acoustics, Speech, and
Signal Processing
(ICASSP), 2005 [pdf] [pdf, in French]
2004
F. Bach, M. I. Jordan. Blind one-microphone
speech separation:
A spectral learning approach. Advances
in Neural Information Processing Systems (NIPS) 17, 2004.
[pdf] [speech
samples] [slides]
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, M. I. Jordan.
Learning graphical models for stationary time series,
IEEE Transactions on Signal Processing, vol. 52, no.
8, 2189-2199, 2004. [pdf]
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]
K. Fukumizu, F. Bach, M. I. Jordan. Dimensionality
reduction
for supervised learning with reproducing kernel Hilbert spaces,
Journal of
Machine Learning Research, 5, 73-99, 2004.
[ps.gz] [pdf]
2003
F. Bach, M. I. Jordan. Beyond independent components: trees and clusters, Journal of Machine Learning Research, 4, 1205-1233, 2003. [pdf] [ps.gz] [matlab code]
F. Bach, M. I. Jordan. Learning spectral clustering, Advances in Neural Information Processing Systems (NIPS) 16, 2004. [pdf] [ps.gz] [tech-report]
Kenji Fukumizu, F. Bach, and M. I. Jordan. Kernel dimensionality reduction
for supervised learning, Advances
in Neural Information Processing Systems (NIPS) 16, 2004.
[ps.gz] [pdf]
[pdf, in Japanese]
F. Bach, M. I. Jordan. Analyse en composantes indépendantes et réseaux Bayésiens, Dix-neuvième colloque GRETSI sur le traitement du signal et des images, 2003. [ps] [pdf] [matlab code]
F. Bach, M. I. Jordan. Finding clusters in independent component analysis, Fourth International Symposium on Independent Component Analysis and Blind Signal Separation, 2003. [ps] [pdf] [matlab code]
F. Bach, M. I. Jordan. Kernel independent component
analysis,
Proceedings of the International Conference on
Acoustics, Speech, and
Signal Processing
(ICASSP), 2003 [ps]
[pdf] [long
version (pdf)]
[matlab code]
2002
F. Bach, M. I. Jordan. Learning graphical models with
Mercer kernels, Advances in Neural Information
Processing Systems (NIPS) 15, 2003.
[ps.gz] [pdf]
F. Bach, M. I. Jordan.
Kernel independent component analysis, Journal
of Machine Learning Research, 3, 1-48, 2002. [ps.gz] [pdf]
[matlab code]
F. Bach, M. I. Jordan. Tree-dependent
component analysis,
Uncertainty in Artificial Intelligence (UAI):
Proceedings of the
Eighteenth Conference, 2002.
[ps.gz] [pdf]
[matlab code]
2001
F. Bach, M. I. Jordan.
Thin junction trees,
Advances in Neural Information Processing Systems (NIPS) 14,
2002. [ps.gz]
[pdf]
Software:
Kernel independent component
analysis - version 1.2 (matlab)
Tree-dependent component
analysis - version 1.0 (matlab)
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)
Diffrac - version 1.0 (matlab)
Hierarchical kernel learning - version 3.0 (matlab)
