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Francis Bach INRIA - SIERRA
project-team CS 81321 Directions to my office (English)
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September
2012: Machine
Learning Summer School, Kyoto - Learning with submodular functions
[slides] [notes]
July
2012: Computer
Vision and Machine Learning Summer School, Grenoble - Kernel
methods and sparse methods for computer vision
July 2012:
International Computer
Vision Summer School, Sicily - Structured
sparsity through convex optimization
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
2013: Statistical
machine learning - Filiere Math/Info - L3 - Ecole Normale Superieure
(Paris)
Fall
2012: An
introduction to graphical models - Master M2 "Mathématiques,
Vision,Apprentissage"
- Ecole Normale Supérieure de Cachan
Spring
2012: Statistical
machine learning - Filiere Math/Info - L3 - Ecole Normale Superieure
(Paris)
Spring 2012: Statistical machine learning - Master M2 "Probabilites et Statistiques" - Universite Paris-Sud (Orsay)
Fall
2011: An
introduction to graphical models - Master M2 "Mathématiques,
Vision,Apprentissage"
- Ecole Normale Supérieure de Cachan
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, Engineer at Google
Toby Hocking,
Postdoctoral Researcher, Tokyo Institute of Technology
Rodolphe
Jenatton, Criteo
Armand Joulin, Postdoctoral
Researcher, Stanford University
Augustin Lefevre,
Postdoctoral Researcher, Universite Catholique de Louvain
Ronny
Luss, IBM Research
Julien
Mairal, Researcher at INRIA, Grenoble
Guillaume Obozinski,
Researcher at INRIA, Ecole Normale Superieure
Mikhail Zaslavskiy, Researcher at Cellectis
2012
K. S. Sesh Kumar and F. Bach. Convex Relaxations for Learning Bounded Treewidth Decomposable Graphs. Technical report, HAL 00763921, 2012. To appear in Proceedings of the International Conference on Machine Learning (ICML). [pdf]
F. Bach. Duality between subgradient and
conditional gradient methods. Technical report, HAL 00757696,
2012. [pdf]
R. Jenatton, R. Gribonval and F. Bach. Local
stability and robustness of sparse dictionary learning in the presence
of noise. Technical report, HAL 00737152, 2012. [pdf]
F. Bach. Sharp
analysis of low-rank kernel matrix approximations. Technical
report, HAL 00723365, 2012.
G. Obozinski and F. Bach. Convex
Relaxation for Combinatorial Penalties. Technical report, HAL
00694765, 2012. [pdf]
N. Le Roux, M. Schmidt, F. Bach. A
Stochastic Gradient Method with an Exponential Convergence Rate for
Strongly-Convex Optimization with Finite Training Sets. Advances
in Neural Information Processing Systems (NIPS). Technical
report, HAL 00674995, 2012.
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]
M. Solnon, S. Arlot, F. Bach. Multi-task
Regression using Minimal Penalties. Journal
of Machine Learning Research, 13(Sep):2773−2812,
2012. [pdf]
A. Joulin and F. Bach. A convex
relaxation for weakly supervised classifiers. Proceedings
of the International Conference on Machine Learning (ICML), 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]
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]
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]
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]
A. Joulin, F. Bach, J.Ponce. Discriminative
Clustering
for Image 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. 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]
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)
