Francis Bach INRIA - Willow project 75230 Paris Cedex
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Cours:
1er semestre 2005-2006:
Introduction aux modèles graphiques - Master M2 "Mathématiques,
Vision, Apprentissage" - Ecole Normale Supérieure de Cachan
1er semestre 2006-2007:
Introduction aux modèles graphiques - Master M2 "Mathématiques,
Vision, Apprentissage" - Ecole Normale Supérieure de Cachan
1er semestre 2007-2008:
Introduction aux modèles graphiques - Master M2 "Mathématiques,
Vision, Apprentissage" - Ecole Normale Supérieure de Cachan
Mai 2007: Modélisation probabiliste et modèles graphiques - Enseignement Spécialisé - Ecole des Mines de Paris
September 2008: Machine Learning Summer School - Ile de Re - Learning with sparsity inducing norms
Groupe de lecture Apprentissage - ParisTech
Publications:
2008
F. Bach. Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning. Advances in Neural Information Processing Systems (NIPS) 20, to appear, 2008. [HAL tech-report]
K. Fukumizu, F. R. Bach, and M. I. Jordan. Kernel dimension reduction in regression. Annals of Statistics, to appear, 2008. [pdf]
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]
J. Abernethy, F. Bach, T. Evgeniou, and J.-P. Vert, A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization. Technical Report HAL-00250231, 2008. [pdf]
M. Zaslavskiy, F. Bach and J.-P. Vert, A path following algorithm for the graph matching problem. Technical Report HAL-00232851, 2008. [pdf]
A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. SimpleMKL, Technical report HAL-00218338, 2008, to appear in Journal of Machine Learning Research. [pdf] [code]
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
Francis R. Bach, Active learning for misspecified generalized linear models, Advances in Neural Information Processing Systems (NIPS) 19, 2006. [pdf] [rapport technique]
Francis R. Bach, Michael I. Jordan, Learning spectral clustering, with application to speech separation, Journal of
Machine Learning Research, 7, 1963-2001, 2006. [pdf] [examples de signaux]
Francis R. Bach, David Heckerman, Eric Horvitz, Considering cost asymmetry in learning classifiers, Journal of Machine Learning Research, 7, 1713-1741, 2006. [pdf]
Kenji Fukumizu, Francis R. Bach, Arthur Gretton. Consistency of Kernel
Canonical Correlation Analysis. Advances in Neural Information Processing Systems (NIPS) 18, 2005.
[pdf] [rapport technique]
Francis R. Bach, Michael I. Jordan. Predictive
low-rank decomposition for kernel methods.
Proceedings of the Twenty-second International Conference on
Machine Learning (ICML), 2005. [pdf]
[code
Matlab/C] [présentation]
Francis R. Bach, Michael I. Jordan. A probabilistic interpretation of canonical correlation analysis. Technical Report 688, Department of Statistics, University of California, Berkeley, 2005 [pdf]
Francis R. Bach, David Heckerman, Eric
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] [présentation]
Francis R. Bach, Michael 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,
en français]
2004
Francis R. Bach,
Michael I. Jordan. Blind one-microphone speech separation:
A spectral learning approach. In press,
Advances in Neural Information Processing Systems (NIPS) 17, 2004.
[pdf] [exemples]
[présentation]
Francis R. Bach, Romain Thibaux, Michael I. Jordan. Computing regularization paths for learning multiple kernels.. In press, Advances in Neural Information Processing Systems (NIPS) 17, 2004. [pdf] [code matlab] [slides]
Francis R. Bach, Michael I. Jordan.
Learning graphical models for stationary time series,
IEEE Transactions on Signal Processing, vol. 52, no. 8, 2189-2199, 2004. [pdf]
Francis R. Bach, Gert R. G. Lanckriet, Michael 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]
Kenji Fukumizu,
Francis R. Bach, Michael 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
Francis R. Bach, Michael I. Jordan. Beyond independent components: trees and clusters, Journal of Machine Learning Research, 4, 1205-1233, 2003. [pdf] [ps.gz] [code matlab]
Francis R. Bach, Michael I. Jordan. Learning spectral clustering, Advances in Neural Information Processing Systems (NIPS) 16, 2004. [pdf] [ps.gz] [tech-report]
Kenji Fukumizu,
Francis R. Bach, and Michael I. Jordan. Kernel dimensionality reduction
for supervised learning,
Advances in Neural Information Processing Systems (NIPS) 16, 2004.
[ps.gz] [pdf]
[pdf, en japonais]
Francis R. Bach, Michael 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]
Francis R. Bach, Michael I. Jordan. Finding clusters in independent component analysis, Fourth International Symposium on Independent Component Analysis and Blind Signal Separation, 2003. [ps] [pdf] [code matlab]
Francis R. Bach, Michael I. Jordan. Kernel independent component analysis,
Proceedings of the International Conference on Acoustics, Speech, and
Signal Processing
(ICASSP), 2003 [ps]
[pdf]
[version longue (pdf)] [code
matlab]
2002
Francis R. Bach, Michael I. Jordan. Learning graphical models with
Mercer kernels,
Advances in Neural Information Processing Systems (NIPS) 15, 2003.
[ps.gz] [pdf]
Francis R. Bach, Michael I. Jordan.
Kernel independent component analysis, Journal of Machine Learning Research, 3, 1-48, 2002. [ps.gz] [pdf]
[code matlab]
Francis R. Bach, Michael I. Jordan. Tree-dependent component analysis,
Uncertainty in Artificial Intelligence (UAI): Proceedings of the
Eighteenth Conference, 2002.
[ps.gz] [pdf]
[code matlab]
2001
Francis R. Bach, Michael I. Jordan.
Thin junction trees,
Advances in Neural Information Processing Systems (NIPS) 14, 2002. [ps.gz]
[pdf]
2000
Francis Bach et Dimitri Spoliansky, La télévision et le
ministre, Gazette de la Société et des Techniques, Juillet 2000.
[pdf] [version
longue]
Logiciels:
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)