- Matrix Sparsity -Structured Sparsity Francis Bach -Guillaume Obozinski
- Journal of Machine Learning Research 9 (2008) 1269-1294 Submitted 11/07; Revised 5/08; Published 7/08 Optimal Solutions for Sparse Principal Component Analysis
- Kernel methods & sparse methods for computer vision
- FINDING CLUSTERS IN INDEPENDENT COMPONENT ANALYSIS Francis R. Bach
- Journal of Machine Learning Research 4 (2003) 12051233 Submitted 11/02; Published 12/03 Beyond Independent Components: Trees and Clusters
- Asymptotically Optimal Regularization in Smooth Parametric Models
- Kernel Change-point Analysis Zaid Harchaoui
- Supervised learning for computer vision: Theory and algorithms -Introduction
- Orientation guide -visitors INRIA Agents
- On the Path to an Ideal ROC Curve: Considering Cost Asymmetry in Learning Classifiers
- Sparse methods for machine learning Francis Bach
- Supervised learning for computer vision: Theory and algorithms -Part II
- 1 Convex Optimization with Sparsity-Inducing Norms
- Online Learning for Latent Dirichlet Allocation Matthew D. Hoffman
- Structured sparse methods for matrix factorization
- Journal of Microscopy, Vol. 239, Pt 2 2010, pp. 159166 doi: 10.1111/j.1365-2818.2010.03365.x Received 1 July 2008; accepted 17 December 2009
- Hierarchical kernel learning Francis Bach
- A Path Following Algorithm for the Graph Matching Problem
- Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning
- DIFFRAC : a discriminative and flexible framework for clustering
- More Efficiency in Multiple Kernel Learning Alain Rakotomamonjy alain.rakotomamonjy@insa-rouen.fr
- Image Classification with Segmentation Graph Kernels Zaid Harchaoui
- Low-rank matrix factorization with attributes Jacob Abernethy
- Active learning for misspecified generalized linear models
- Statistical Convergence of Kernel CCA Kenji Fukumizu
- Predictive low-rank decomposition for kernel Francis Bach
- Journal of Machine Learning Research 5 (2004) 73-99 Submitted 12/01; Revised 11/02; Published 1/04 Dimensionality Reduction for Supervised Learning with
- KERNEL INDEPENDENT COMPONENT ANALYSIS Francis R. Bach
- Learning Graphical Models with Mercer Kernels
- Tree-dependent Component Analysis Francis R. Bach
- Sparse methods for machine learning Theory and algorithms
- Methodes `a noyaux en apprentissage statistique
- Copyright by SIAM. Unauthorized reproduction of this article is prohibited. SIAM J. OPTIM. c 2010 Society for Industrial and Applied Mathematics
- FINDING CLUSTERS IN INDEPENDENT COMPONENT ANALYSIS Francis R. Bach
- IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 52, NO. 8, AUGUST 2004 2189 Learning Graphical Models for
- Automatic Annotation of Human Actions in Video Olivier Duchenne, Ivan Laptev, Josef Sivic, Francis Bach and Jean Ponce
- Active learning for misspecified generalized linear models
- Kernel Dimensionality Reduction for Supervised Kenji Fukumizu
- Optimal solutions for Sparse Principal Component Analysis
- Supervised learning for computer vision: Theory and algorithms -Part I
- Multiple Kernel Learning, Conic Duality, and the SMO Algorithm Francis R. Bach & Gert R. G. Lanckriet {fbach,gert}@cs.berkeley.edu
- KERNEL INDEPENDENT COMPONENT ANALYSIS Francis R. Bach
- Online Dictionary Learning for Sparse Coding Julien Mairal JULIEN.MAIRAL@INRIA.FR
- Thin Junction Trees Francis R. Bach
- Sparse Image Representation with Epitomes Louise Benot1,3
- Learning Spectral Clustering Francis R. Bach
- On the Path to an Ideal ROC Curve: Considering Cost Asymmetry in Learning Classifiers
- Proximal Methods for Sparse Hierarchical Dictionary Learning Rodolphe Jenatton1
- Kernel Dimensionality Reduction for Supervised Kenji Fukumizu
- A reduced model for simulating grain growth Reiner Henseler, Barbara Niethammer, Felix Otto
- Journal of Machine Learning Research 9 (2008) 1179-1225 Submitted 7/07; Revised 3/08; Published 06/08 Consistency of the Group Lasso and Multiple Kernel Learning
- Bolasso: Model Consistent Lasso Estimation through the Bootstrap Francis R. Bach FRANCIS.BACH@MINES.ORG
- Electronic Journal of Statistics Vol. 4 (2010) 384414
- Structured Sparse Principal Component Analysis Rodolphe Jenatton
- Learning Graphical Models with Mercer Kernels
- Discriminative Learned Dictionaries for Local Image Analysis Julien Mairal1,5
- Network Flow Algorithms for Structured Sparsity Julien Mairal
- Testing for Homogeneity with Kernel Fisher Discriminant Analysis
- Francis Bach http://www.di.ens.fr/~fbach/ francis.bach@mines.org
- Learning Spectral Clustering Francis R. Bach
- Discriminative clustering for image co-segmentation Armand Joulin1,2,3
- Efficient Optimization for Discriminative Latent Class Models
- Francis Bach http://www.di.ens.fr/~fbach/ francis.bach@mines.org
- Learning with sparsity-inducing norms Francis Bach
- A Tensor-Based Algorithm for High-Order Graph Matching Olivier Duchenne1,4
- Journal of Machine Learning Research 8 (2008) 1019-1048 Submitted 10/07; Revised 3/08; Published 6/08 Consistency of Trace Norm Minimization
- Sparse methods for machine learning Theory and algorithms
- Mod`eles de Markov caches pour l'estimation de plusieurs frequences fondamentales
- Learning Spectral Clustering Francis R. Bach
- Journal of Machine Learning Research 5 (2004) 7399 Submitted 12/01; Revised 11/02; Published 1/04 Dimensionality Reduction for Supervised Learning with
- Analyse en Composantes Independantes et Reseaux Bayesiens Francis R. BACH1, Michael I. JORDAN2,
- Discriminative Clustering for Image Co-segmentation
- DIFFRAC : a discriminative and flexible framework for clustering
- Graph Kernels between Point Clouds Francis R. Bach FRANCIS.BACH@MINES.ORG
- Clustered Multi-Task Learning: a Convex Formulation
- Journal of Machine Learning Research 11 (2010) 19-60 Submitted 7/09; Revised 11/09; Published 1/10 Online Learning for Matrix Factorization and Sparse Coding
- Journal of Machine Learning Research 3 (2002) 1-48 Submitted 11/01; Published 07/02 Kernel Independent Component Analysis
- Non-local Sparse Models for Image Restoration Julien Mairal1,5
- Journal of Machine Learning Research 7 (2006) 17131741 Submitted 10/05; Revised 7/06; Published 8/06 Considering Cost Asymmetry in Learning Classifiers
- Full Regularization Path for Sparse Principal Component Analysis Alexandre d'Aspremont aspremon@princeton.edu
- Treedependent Component Analysis Francis R. Bach
- Computing regularization paths for learning multiple kernels
- Fast Kernel Learning using Sequential Minimal Optimization
- SVM Speaker Verification using an Incomplete Cholesky Decomposition Sequence Kernel
- Thin Junction Trees Francis R. Bach
- Predictive low-rank decomposition for kernel methods Francis R. Bach francis.bach@mines.org
- Analyse en Composantes Ind ependantes et R eseaux Bay esiens Francis R. BACH 1 , Michael I. JORDAN 2 ,
- Sparse probabilistic projections Cedric Archambeau
- A Probabilistic Interpretation of Canonical Correlation Analysis
- DISCRIMINATIVE TRAINING OF HIDDEN MARKOV MODELS FOR MULTIPLE PITCH TRACKING
- Supervised Dictionary Learning Julien Mairal
- Sparse methods for machine learning Theory and algorithms
- Journal of Machine Learning Research 4 (2003) 1205-1233 Submitted 11/02; Published 12/03 Beyond Independent Components: Trees and Clusters
- Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation
- BIOINFORMATICS Vol. 00 no. 00 2006
- Kernel Dimension Reduction in Kenji Fukumizu
- Learning Mid-Level Features For Recognition Y-Lan Boureau1,3,4
- Consistency of group lasso and multiple kernel learning
- ITAKURA-SAITO NONNEGATIVE MATRIX FACTORIZATION WITH GROUP SPARSITY Augustin Lef`evre
- Clusterpath: An Algorithm for Clustering using Convex Fusion Penalties Toby Dylan Hocking TOBY.HOCKING@INRIA.FR
- Ask the locals: multi-way local pooling for image recognition Y-Lan Boureau1,3,
- Structured sparsity-inducing norms through submodular functions
- Kernel methods & sparse methods for computer vision
- Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization
- Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning
- Trace Lasso: a trace norm regularization for correlated designs
- Francis Bach francis.bach@ens.fr
- Francis Bach francis.bach@ens.fr
- Foundations and Trends R Machine Learning
