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Francis Bach INRIA - SIERRA
project-team CS 81321 Directions to my office (English)
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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)
Spring 2011: Statistical machine learning - Master M2 "Probabilites et Statistiques" - Universite Paris-Sud (Orsay)
Spring 2010: Statistical machine learning - Master M2 "Probabilites et Statistiques" - Universite Paris-Sud (Orsay)
Fall
2008:
An
introduction to graphical models - Master M2 "Mathématiques,
Vision, Apprentissage" - Ecole Normale Supérieure de Cachan
May 2008: Probabilistic
modelling and graphical models: Enseignement Spécialisé - Ecole
des Mines de Paris
SMILE:
Statistical
Machine
Learning in Paris : seminar / reading group
MGA: Projet ANR Modeles graphiques et applications
Timothee Cour, Researcher at NEC
Rodolphe
Jenatton, Postdoctoral Researcher, Ecole Polytechnique
Guillaume Obozinski,
Researcher at INRIA, Ecole Normale Superieure
Mikhail Zaslavskiy, Researcher at Cellectis
Publications
2011
F. Bach. Learning with
Submodular Functions: A Convex Optimization Perspective.
Technical Report HAL 00645271, 2011. Submitted to Foundations
and Trends in Machine Learning. [
F. Bach, R. Jenatton, J. Mairal, G. Obozinski. Optimization
with sparsity-inducing penalties
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.
N. Le Roux, F. Bach. Local
component analysis. Technical report, HAL 00617965, 2011.
[
F. Bach, R. Jenatton, J. Mairal, G. Obozinski. Structured
sparsity through convex optimization. Technical report,
HAL 00621245, 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]
J. Mairal, F. Bach, J. Ponce. Task-Driven
Dictionary
Learning. Technical report, HAL : inria-00521534,
2011. To appear in
IEEE
Transactions on Pattern Analysis and Machine Intelligence.
[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]
S. Arlot, F. Bach. Data-driven
Calibration of Linear Estimators with Minimal Penalties. Technical
report, HAL 00414774-v2, 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]
R. Jenatton, A. Gramfort, V. Michel, G. Obozinski, E. Eger, F.
Bach, B. Thirion. Multi-scale
Mining of fMRI data with Hierarchical Structured Sparsity.
Technical report, HAL-inria-00589785, 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]
A. Joulin, F. Bach, J.Ponce. Efficient
Optimization
for Discriminative Latent Class Models. Advances
in Neural Information Processing Systems (NIPS), 2010. [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]
A. Joulin, F. Bach, J.Ponce. Discriminative
Clustering
for Cmage Co-segmentation. Proceedings
of the Conference on Computer Vision and Pattern Recognition
(CVPR), 2010. [pdf]
[slides] [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]
M. Zaslavskiy, F. Bach and J.-P. Vert. Many-to-Many
Graph
Matching: a Continuous Relaxation Approach. Technical
report
HAL-00465916, 2010. To appear in Proceedings
of the European Conference on
Machine Learning (ECML), [pdf]
F. Bach. Self-Concordant
Analysis for Logistic Regression. Electronic
Journal of Statistics, 4, 384-414, 2010. [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]
[code]
A. Cord, F. Bach, D. Jeulin. Texture
classification
by statistical learning from morphological
image processing: application to metallic surfaces. Journal
of Microscopy, 239(2), 159–166, 2010. [pdf]
2009
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] [slides]
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]
[code]
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]
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]
Discriminative clustering for image co-segmentation (matlab/C)
Structured variable selection with sparsity-inducing norms (matlab)
Structured sparse PCA (matlab)
Sparse modeling software - SPAM (C)
Hierarchical kernel learning - version 3.0 (matlab)
Diffrac - version 1.0 (matlab)
Grouplasso - version 1.0 (matlab)
SimpleMKL - version 1.0 (matlab)
Support Kernel Machine - Multiple kernel learning (matlab)
Predictive low-rank decomposition for kernel methods - version 1.0 (matlab/C)
Computing regularization paths for multiple kernel learning - version 1.0 (matlab)
Tree-dependent component analysis - version 1.0 (matlab)
