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Francis Bach INRIA - SIERRA
project-team PSL Research University CS61534 75647 Paris Cedex
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Blog - Tutorials - Courses - Students - Alumni - Publications - Software
Book: Learning Theory from First
Principles, published
in December 2024 at MIT Press
Final pdf version
Code (python, Matlab)
Blog post
I am
a researcher at INRIA, leading since 2011 the SIERRA project-team, which is part of the Computer Science Department at Ecole Normale Supérieure, and a joint team between CNRS, ENS and INRIA. I completed my Ph.D. in Computer
Science at U.C. Berkeley, working with Professor Michael Jordan, and spent two years in the Mathematical Morphology group at Ecole des Mines de Paris, I then joined the WILLOW project-team at INRIA/Ecole
Normale Superieure/CNRS from 2007 to 2010. From 2009 to 2014, I was running the
ERC project SIERRA, and I am now running
the ERC project SEQUOIA. I have been elected in 2020 at the French Academy of Sciences. I am interested
in statistical machine learning, and especially in optimization, sparse
methods, kernel-based learning, neural networks, graphical models, and signal
processing. [CV (English)] [CV (French)] [short bio][short bio (francais)]
Tutorials / mini-courses
(recent - older ones below)
April 2025: Graduate
School in Systems, Optimization, Control and Networks (SOCN) [board-1] [board-2]
[board-3] [board-4]
[board-5] [board-6]
[exercises]
September 2024: CIME
School on High-Dimensional Approximation [notes]
September 2023: Summer
school on distributed learning [slides]
July 2021: PRAIRIE/MIAI AI summer school [slides]
September 2020: Hausdorff School, MCMC: Recent developments and new connections
- Large-scale machine learning and convex optimization [slides]
June 2020: Machine Learning Summer School, Tubingen - Large-scale
machine learning and convex optimization [slides]
Courses (recent - older
ones below)
Fall 2024: Learning theory from first principles - Mastere M2 IASD
Fall 2023: Learning theory from first principles - Mastere M2 Mash
Fall 2022: Learning theory from first principles - Mastere M2 Mash
Spring 2022: Optimisation et Apprentissage Statistique - Master
M2 "Mathematiques de l'aleatoire" - Universite Paris-Sud (Orsay)
Fall 2021: Learning theory from first principles - Mastere M2 Mash
Spring 2021: Statistical machine learning - Ecole Normale
Superieure (Paris)
Spring 2021: Machine Learning - Masters ICFP, Ecole
Normale Superieure
Spring 2021: Optimisation et Apprentissage Statistique - Master
M2 "Mathematiques de l'aleatoire" - Universite Paris-Sud (Orsay)
PhD Students and Postdocs
Eliot Beyler
Eugène
Berta, co-advised with Michael
Jordan
Nabil Boukir, co-advised with Michael Jordan
Sacha Braun, co-advised with Michael
Jordan
Juliette Decugis, co-advised with Gabriel
Synnaeve and Taco Cohen
Alexandre François, co-advised with Antonio
Orvieto
David
Holzmüller
Etienne Gauthier, co-advised with Michael Jordan
Frederik Kunstner
Simon Martin, co-advised with Giulio Biroli
Fabian Schaipp, co-advised with Umut Simsekli and Adrien Taylor
Corbinian Schlosser, co-advised with Alessandro Rudi
Lawrence Stewart, co-advised
with Jean-Philippe Vert
Alumni
Martin
Arjovsky, Research scientist, Deepmind
Dmitry Babichev, Researcher, Huawei
P Balamurugan, Assistant Professor, Indian Institute of Technology, Bombay
Anaël Beaugnon, Data scientist, Roche
Amit Bermanis, Senior Algorithms Researcher, ThetaRay
Eloïse Berthier, Researcher at ENSTA, Paris
Raphaël Berthier, tenure-track research
faculty, Inria Sorbonne Université
Alberto Bietti, Research scientist, Flat Iron
Institute, New York
Vivien Cabannes, Research Scientist, Meta, Paris
Lénaïc Chizat, Assistant Professor, EPFL,
Ecole Polytechnique Fédérale de Lausanne
Timothee Cour, Engineer at Google
Hadi Daneshmand, Post-doctoral fellow, MIT
Alexandre Défossez, Research scientist, Kyutai, Paris
Aymeric Dieuleveut, Professor, Ecole Polytechnique,
Palaiseau
Christophe Dupuy, Amazon, Cambridge, USA
Bertille Follain
Pascal Germain, Assistant professor, Université Laval, Canada
Robert Gower, Research Scientist, Flatiron
Institute, New York
Edouard Grave, Research scientist, Kyutai, Paris
Zaid Harchaoui, Associate Professor, University of Washington
Hadrien Hendrikx, Researcher, Inria
Grenoble
Toby Hocking,
Assistant Professor, Northern Arizona University
Nicolas
Flammarion, Assistant Professor, Ecole Polytechnique Federale de Lausanne
Fajwel Fogel, Research scientist, Sancare
Rodolphe Jenatton, CTO, Bioptimus
Armand Joulin, Research scientist, Google Deepmind, Paris
Hans Kersting, Research Scientist, Yahoo Research
Ziad Kobeissi, Researcher at Inria,
Saclay
Sesh Kumar, Research fellow, Imperial College
Business School
Simon Lacoste-Julien, Professor, Université
de Montréal
Remi
Lajugie, Professeur d'Informatique, Lycée Janson de Sailly, Paris
Marc Lambert
Augustin Lefèvre, Data scientist, YKems
Ivan Lerner
Nicolas Le Roux, Researcher, Microsoft Research, Montréal
Ronny Luss, Researcher, IBM Research
Julien Mairal, Researcher at Inria, Grenoble
Ulysse Marteau-Ferey, Research
scientist, Owkin
Bamdev Mishra, Research scientist, Amazon Bangalore
Céline Moucer,
Ministère du Budget
Boris Muzellec, Research scientist,
Owkin
Anil Nelakanti, Research scientist, Amazon Bangalore
Alex Nowak-Vila, Research scientist,
Owkin
Guillaume Obozinski, Deputy Chief
Data Scientist, Swiss Data Science Center
Dmitrii Ostrovskii, Postdoctoral
researcher, University of South California
Loucas Pillaud-Vivien, Postdoctoral researcher, Ecole Polytechnique
Federale de Lausanne
Anastasia Podosinnikova, Postdoctoral fellow, MIT
Fabian
Pedregosa, Researcher, Google Brain, Montréal
Rafael Rezende, Postdoctoral researcher, Naver Labs, Grenoble
Anant Raj, Assistant Professor, Indian Institute of Science, Bangalore, India
Théo Ryffel, Arkhn
Thomas Schatz, Assistant professor, Aix-Marseille Université, Marseille
Mark Schmidt, Associate professor,
University of British Columbia
Damien Scieur, Research scientist, Samsung, Montreal
Nino Shervashidze, Data scientist, Sancare
Tatiana Shpakova, Researcher, Huawei
Matthieu Solnon, Professeur de Mathématiques, CPGE, Lycée Lavoisier
Adrien Taylor, Research scientist,
Inria Paris
Blake Woodworth, Assistant Professor, George
Washington University
Mikhail Zaslavskiy, Byopt
Publications
2025
Sacha Braun, Liviu Aolaritei, Michael I.
Jordan, Francis Bach. Minimum Volume Conformal Sets for Multivariate
Regression. Technical report, arXiv:2503.19068, 2025. [pdf]
Etienne Gauthier, Francis Bach, Michael
I. Jordan. E-Values Expand the Scope of Conformal
Prediction. Technical report, arXiv:2503.13050,
2025. [pdf]
Eliot Beyler, Francis Bach. Optimal Denoising in Score-Based Generative
Models: The Role of Data Regularity. Technical report, arXiv:2503.12966,
2025. [pdf]
Léo Dana, Francis Bach, Loucas Pillaud-Vivien. Convergence of Shallow
ReLU Networks on Weakly Interacting Data. Technical report, arXiv:2502.16977,
2025. [pdf]
Nathan Doumèche, Francis Bach, Éloi Bedek, Gérard Biau, Claire Boyer,
Yannig Goude. Forecasting time series with constraints. Technical
report, arXiv:2502.10485, 2025. [pdf]
Alexandre François, Antonio Orvieto, Francis Bach. An Uncertainty
Principle for Linear Recurrent Neural Networks. Technical report,
arXiv:2502.09287, 2025. [pdf]
Etienne Gauthier, Francis Bach, Michael I. Jordan. Statistical Collusion
by Collectives on Learning Platforms. Technical report, arXiv:2502.04879,
2025. [pdf]
Lawrence Stewart, Francis Bach, Quentin Berthet. Building Bridges between
Regression, Clustering, and Classification. Technical report, arXiv:2502.02996,
2025. [pdf]
Francis Bach, Saeed Saremi. Sampling Binary Data by Denoising through
Score Functions. Technical report, arXiv:2502.00557, 2025. [pdf]
Eugène Berta, David Holzmüller, Michael I. Jordan, Francis Bach. Rethinking
Early Stopping: Refine, Then Calibrate. Technical report, arXiv:2501.19195,
2025. [pdf] [slides]
Fabian Schaipp, Alexander Hägele, Adrien Taylor, Umut Simsekli, Francis Bach.
The Surprising Agreement Between Convex Optimization Theory and Learning-Rate
Scheduling for Large Model Training. Technical report, arXiv:2501.18965,
2025. [pdf]
Sebastian G. Gruber, F. Bach. Optimizing Estimators of Squared Calibration
Errors in Classification. Transactions of Machine Learning Research,
2025. [pdf]
E. Beyler, F. Bach. Variational Inference on the Boolean Hypercube with
the Quantum Entropy. Proceedings of the International Conference on
Artificial Intelligence and Statistics (AISTATS), 2025. [pdf]
A. H. Ribeiro, T. B. Schön, D. Zachariah, F. Bach. Efficient Optimization
Algorithms for Linear Adversarial Training. Proceedings of the
International Conference on Artificial Intelligence and Statistics (AISTATS),
2025. [pdf]
2024
N. Doumèche, F. Bach, G. Biau, C. Boyer. Physics-informed kernel learning.
Technical report, arXiv:2409.13786, 2024. [pdf]
C. Moucer, A. Taylor, F. Bach. Constructive approaches to concentration inequalities
with independent random variables. Technical report, arXiv:2408.16480,
2024. [pdf]
C. Chazal, A. Korba, F. Bach. Statistical and Geometrical properties of regularized
Kernel Kullback-Leibler divergence. Advances in Neural Information
Processing Systems (NeurIPS), 2024. [pdf]
B. Follain, F. Bach. Enhanced Feature Learning via Regularisation: Integrating
Neural Networks and Kernel Methods. Technical report, arXiv:2407.17280,
2024. [pdf]
S. Bonnabel, M. Lambert, F. Bach. Low-rank plus diagonal approximations for
Riccati-like matrix differential equations. SIAM Journal on Matrix
Analysis and Applications, 45(3):1669-1688, 2024. [pdf]
M. Lambert, S. Bonnabel, F. Bach. Variational Dynamic Programming for Stochastic
Optimal Control. Conference on Decision and Control, 2024. [pdf]
C. Moucer, A. Taylor, F. Bach. Geometry-dependent matching pursuit: a transition
phase for convergence on linear regression and LASSO. Technical report,
arXiv:2403.03072, 2024. [pdf]
N. Doumèche, F. Bach, G. Biau, C. Boyer. Physics-informed machine learning
as a kernel method. Proceedings of the Conference on Learning Theory
(COLT), 2024. [pdf]
E. Berta, F. Bach, M. I. Jordan. Classifier Calibration with ROC-Regularized
Isotonic Regression. Proceedings of the International Conference on
Artificial Intelligence and Statistics (AISTATS), 2024. [pdf]
S. Martin, F. Bach, G. Biroli. On the Impact
of Overparameterization on the Training of a Shallow Neural Network in High
Dimensions. Proceedings of the International Conference on Artificial
Intelligence and Statistics (AISTATS), 2024. [pdf]
V. Cabannes, F. Bach. The Galerkin method beats Graph-Based Approaches for Spectral
Algorithms. Proceedings of the International Conference on Artificial
Intelligence and Statistics (AISTATS), 2024. [pdf]
[code]
S. Saremi, J.-W. Park, F. Bach. Chain of
Log-Concave Markov Chains. Proceedings of the International Conference
on Learning Representations (ICLR), 2024. [pdf]
[slides]
U. Marteau-Ferey, F. Bach, A. Rudi. Second Order Conditions to Decompose
Smooth Functions as Sums of Squares. SIAM Journal on Optimization,
34:616-641, 2024. [pdf]
F. Bach. High-dimensional analysis of double descent for linear
regression with random projections. SIAM Journal on Mathematics of
Data Science, 6(1):26-50, 2024. [pdf]
F. Bach. Sum-of-squares relaxations for information theory and
variational inference. Foundations of Computational Mathematics,
2024. [pdf]
F. Bach. Sum-of-squares relaxations for polynomial min-max problems
over simple sets. Mathematical Programming, 2024. [pdf]
A. Rudi, U. Marteau-Ferey, F. Bach. Finding Global Minima via Kernel Approximations.
Mathematical Programming, 2024. [pdf]
A. Vacher, B. Muzellec, F. Bach, F.-X. Vialard, A. Rudi. Optimal Estimation
of Smooth Transport Maps with Kernel SoS. SIAM Journal on Mathematics of
Data Science, 6(2):311-342, 2024. [pdf]
B. Follain, F. Bach. Nonparametric Linear Feature Learning in Regression
Through Regularisation. Electronic Journal of Statistics, 18(2):4075-4118.
[pdf]
2023
A. Joudaki, H. Daneshmand, F. Bach. On the impact of activation and normalization
in obtaining isometric embeddings at initialization. Advances in Neural
Information Processing Systems (NeurIPS), 2023. [pdf]
L. Stewart, F. Bach, F. Llinares-López, Q. Berthet. Differentiable Clustering
with Perturbed Spanning Forests. Advances in Neural Information
Processing Systems (NeurIPS), 2023. [pdf] [code]
A. H. Ribeiro, D. Zachariah, F. Bach, T. B. Schön. Regularization properties
of adversarially-trained linear regression. Advances in Neural Information
Processing Systems (NeurIPS), 2023. [pdf]
S. Saremi, R. K. Srivastava, F. Bach. Universal
Smoothed Score Functions for Generative Modeling. Technical report, arXiv:2303.11669, 2023. [pdf]
B. Tzen, A. Raj, M. Raginsky, F. Bach. Variational principles for mirror descent
and mirror Langevin dynamics. IEEE Control Systems Letters, 7:1542-1547,
2023. [pdf]
D. Holzmüller, F. Bach. Convergence rates for non-log-concave sampling and
log-partition estimation. Technical report, arXiv:2303.0323, 2023. [pdf]
L. Pillaud-Vivien, F. Bach. Kernelized diffusion maps. Proceedings
of the Conference on Learning Theory (COLT), 2023. [pdf]
B. Woodworth, K. Mishchenko, F. Bach. Two losses are better than one: Faster
optimization using a cheaper proxy. Proceedings of the International Conference
on Machine Learning (ICML), 2023. [pdf]
F. Bach. On the relationship between multivariate splines and
infinitely-wide neural networks. Technical report, arXiv:2302.03459,
2023. [pdf]
F. Bach. Information theory with kernel methods. IEEE
Transactions in Information Theory, 69(2):752-775, 2023. [pdf]
F. Bach, A. Rudi. Exponential convergence of sum-of-squares hierarchies
for trigonometric polynomials. SIAM Journal on Optimization,
33(3): 2137-2159. [pdf]
L. Stewart, F. Bach, Q. Berthet, J.-P. Vert. Regression as classification:
Influence of task formulation on neural network features. Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS), 2023. [pdf]
A. Orvieto, A. Raj, H. Kersting, F. Bach. Explicit
regularization in overparametrized models via noise injection. Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS), 2023. [pdf]
C. Moucer, A. Taylor, F. Bach. A systematic approach to Lyapunov analyses
of continuous-time models in convex optimization. SIAM Journal on
Optimization, 33(3):1558-1586, 2023. [pdf]
M. Lambert, S. Bonnabel, F. Bach. The limited-memory recursive variational
Gaussian approximation (L-RVGA). Statistics and Computing, 33,
2023. [pdf]
2022
A. Défossez, L. Bottou, F. Bach, N. Usunier. A Simple convergence proof
of Adam and Adagrad. Transactions on Machine Learning Research,
2022. [pdf]
A. Lucchi, F. Proske, A. Orvieto, F. Bach, H. Kersting. On the
Theoretical Properties of Noise Correlation in Stochastic Optimization. Advances
in Neural Information Processing Systems (NeurIPS), 2022. [pdf]
K. Mishchenko, F. Bach, M. Even, B. Woodworth. Asynchronous SGD Beats Minibatch
SGD Under Arbitrary Delays. Advances in Neural Information Processing
Systems (NeurIPS), 2022. [pdf]
M. Lambert, S. Chewi, F. Bach, S. Bonnabel, P. Rigollet. Variational inference
via Wasserstein gradient flows. Advances in Neural Information Processing
Systems (NeurIPS), 2022. [pdf]
B. Dubois-Taine, F. Bach, Q. Berthet, A. Taylor. Fast Stochastic Composite
Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization.
Advances in Neural Information Processing Systems (NeurIPS), 2022. [pdf]
V. Cabannes, F. Bach, V. Perchet, A. Rudi. Active Labeling: Streaming Stochastic
Gradients. Advances in Neural Information Processing Systems (NeurIPS),
2022. [pdf]
E. Berthier, Z. Kobeissi, F. Bach. A Non-asymptotic Analysis of Non-parametric
Temporal-Difference Learning. Advances in Neural Information Processing
Systems (NeurIPS), 2022. [pdf]
A. Joudaki, H. Daneshmand, F. Bach. Entropy Maximization with Depth: A Variational
Principle for Random Neural Networks. Technical report, arXiv:2205.13076,
2022. [pdf]
H. Daneshmand, F. Bach. Polynomial-time sparse measure recovery. Technical
report, arXiv:2204.07879, 2022. [pdf]
Z. Kobeissi, F. Bach. On a Variance Reduction Correction of the Temporal
Difference for Policy Evaluation in the Stochastic Continuous Setting. Technical
report, arXiv:2202.07960, 2022. [pdf]
A. Orvieto, H. Kersting, F. Proske, F. Bach, A. Lucchi. Anticorrelated Noise
Injection for Improved Generalization. Proceedings of the International
Conference on Machine Learning (ICML), 2022. [pdf]
T. Ryffel, F. Bach, D. Pointcheval. Differential Privacy Guarantees
for Stochastic Gradient Langevin Dynamics. Technical report, arXiv:2201.11980,
2022. [pdf]
M. Lambert, S. Bonnabel, F. Bach. The continuous-discrete variational Kalman
filter (CD-VKF). IEEE Conference on Decision and Control, 2022. [pdf]
E. Berthier, J. Carpentier, A. Rudi, F. Bach. Infinite-Dimensional
Sums-of-Squares for Optimal Control. IEEE Conference on Decision and
Control, 2022. [pdf]
B. Woodworth, F. Bach, A. Rudi. Non-Convex
Optimization with Certificates and Fast Rates Through Kernel Sums of Squares. Proceedings
of the Conference on Learning Theory (COLT), 2022. [pdf]
A. Raj, F. Bach. Convergence of uncertainty sampling for active learning. Proceedings
of the International Conference on Machine Learning (ICML), 2022. [pdf]
F. Bach, L. Chizat. Gradient Descent on Infinitely Wide Neural Networks:
Global Convergence and Generalization. Proceedings of the International
Congress of Mathematicians, 2022. [pdf]
Y. Sun, F. Bach. Screening for a Reweighted Penalized Conditional
Gradient Method. Open Journal of Mathematical Optimization,
3:1-35, 2022 [pdf]
M. Barré, A. Taylor, F. Bach. A note on approximate accelerated forward-backward
methods with absolute and relative errors, and possibly strongly convex
objectives. Open Journal of Mathematical Optimization, 3(1),
2022. [pdf]
U. Marteau-Ferey, A. Rudi, F. Bach. Sampling from Arbitrary Functions via
PSD Models. Proceedings of the International Conference on Artificial
Intelligence and Statistics (AISTATS), 2022. [pdf]
A. Nowak-Vila, A. Rudi, F. Bach. On the Consistency of Max-Margin
Losses. Proceedings of the International Conference on Artificial
Intelligence and Statistics (AISTATS), 2022. [pdf]
M. Lambert, S. Bonnabel, F. Bach. The recursive variational Gaussian approximation
(R-VGA). Statistics and Computing, 32(1), 2022. [pdf]
2021
B. Muzellec, F. Bach, A. Rudi. Learning PSD-valued functions using kernel
sums-of-squares. Technical report, arXiv:2111.11306, 2021. [pdf]
B. Muzellec, F. Bach, A. Rudi. A
Note on Optimizing Distributions using Kernel Mean Embeddings. Technical report, arXiv:2106.09994, 2021. [pdf]
V. Cabannes, L. Pillaud-Vivien, F. Bach, A. Rudi. Overcoming
the curse of dimensionality with Laplacian regularization in semi-supervised
learning. Advances in
Neural Information Processing Systems (NeurIPS), 2021. [pdf]
H. Daneshmand, A. Joudaki, F. Bach. Batch
Normalization Orthogonalizes Representations in Deep Random Networks. Advances in Neural Information
Processing Systems (NeurIPS), 2021. [pdf]
M. Even, R. Berthier, F. Bach, N. Flammarion,
P. Gaillard, H. Hendrikx, L. Massoulié, A. Taylor. A Continuized
View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized
Gossip. Advances in Neural
Information Processing Systems (NeurIPS),
2021. [pdf]
F. Bach. On the Effectiveness
of Richardson Extrapolation in Data Science. SIAM
Journal on Mathematics of Data Science,
3(4):1251-1277, 2021. [pdf] [slides]
A. Vacher, B. Muzellec, A. Rudi, F. Bach, F.-X.
Vialard. A Dimension-free Computational Upper-bound for Smooth
Optimal Transport Estimation. Proceedings
of the Conference on Learning Theory (COLT),
2021. [pdf]
V. Cabannes, F. Bach, A. Rudi. Fast
rates in structured prediction. Proceedings
of the Conference on Learning Theory (COLT),
2021. [pdf]
V. Cabannes, F. Bach, A. Rudi. Disambiguation
of weak supervision with exponential convergence rates. Proceedings of the International
Conference on Machine Learning (ICML),
2021. [pdf]
A. Bietti, F. Bach. Deep Equals
Shallow for ReLU Networks in Kernel Regimes. Proceedings of the International Conference on Learning
Representations (ICLR), 2021. [pdf]
A. Raj, F. Bach. Explicit Regularization
of Stochastic Gradient Methods through Duality. Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS), 2021. [pdf]
D. Ostrovskii, F. Bach. Finite-sample
Analysis of M-estimators using Self-concordance. Electronic Journal
of Statistics, 15(1):326-391, 2021. [pdf]
E. Berthier, J. Carpentier, F. Bach. Fast and Robust Stability Region Estimation
for Nonlinear Dynamical Systems. Proceedings of the European
Control Conference (ECC), 2021. [pdf]
R. M. Gower, P. Richtárik, F. Bach. Stochastic Quasi-Gradient Methods: Variance
Reduction via Jacobian Sketching. Mathematical Programming, 188:135–192,
2021. [pdf]
2020
U. Marteau-Ferey, F. Bach, A. Rudi. Non-parametric Models for Non-negative
Functions. Advances in Neural Information Processing Systems
(NeurIPS), 2020. [pdf]
H. Hendrikx, F. Bach, L. Massoulié. Dual-Free Stochastic Decentralized Optimization
with Variance Reduction. Advances in Neural Information Processing
Systems (NeurIPS), 2020. [pdf]
R. Berthier, F. Bach, P. Gaillard. Tight Nonparametric Convergence
Rates for Stochastic Gradient Descent under the Noiseless Linear Model. Advances
in Neural Information Processing Systems (NeurIPS), 2020. [pdf]
Q. Berthet, M. Blondel, O. Teboul, M. Cuturi, J.-P. Vert, F. Bach. Learning
with Differentiable Perturbed Optimizers. Advances in Neural Information
Processing Systems (NeurIPS), 2020. [pdf] [video]
H. Daneshmand, J. Kohler, F. Bach, T. Hofmann, A. Lucchi. Batch Normalization
Provably Avoids Rank Collapse for Randomly Initialised Deep Networks. Advances
in Neural Information Processing Systems (NeurIPS), 2020. [pdf]
M. Barré, A. Taylor, F. Bach. Principled Analyses and Design of
First-Order Methods with Inexact Proximal Operators. Technical report,
arXiv:2006.06041, 2020. [pdf]
T. Eboli, A. Nowak-Vila, J. Sun, F. Bach, J. Ponce, A. Rudi. Structured
and Localized Image Restoration. Technical report, arXiv:2006.09261, 2020.
[pdf]
T. Ryffel, D. Pointcheval, F. Bach. ARIANN: Low-Interaction Privacy-Preserving
Deep Learning via Function Secret Sharing. Technical report,
arXiv:2006.04593, 2020. [pdf]
H. Hendrikx, F. Bach, L. Massoulié. An Optimal Algorithm for Decentralized
Finite Sum Optimization. Technical report, arXiv:2005.10675, 2020. [pdf]
R. Sankaran, F. Bach, C. Bhattacharyya. Learning With Subquadratic
Regularization : A Primal-Dual Approach. Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI), 2020. [pdf]
A. Nowak-Vila, F. Bach, A. Rudi. Consistent
Structured Prediction with Max-Min Margin Markov Networks. Proceedings
of the International Conference on Machine Learning (ICML), 2020. [pdf]
V. Cabannes, A. Rudi, F. Bach. Structured Prediction with Partial Labelling
through the Infimum Loss. Proceedings of the International
Conference on Machine Learning (ICML), 2020. [pdf]
H. Hendrikx, L. Xiao, S. Bubeck, F. Bach, L. Massoulié. Statistically
Preconditioned Accelerated Gradient Method for Distributed Optimization. Proceedings
of the International Conference on Machine Learning (ICML), 2020. [pdf] [video]
M. Ballu, Q. Berthet, F. Bach. Stochastic Optimization for Regularized Wasserstein
Estimators. Proceedings of the International Conference on Machine
Learning (ICML), 2020. [pdf]
Y. Sun, F. Bach. Safe Screening for the Generalized Conditional
Gradient Method. Technical report, arXiv:2002.09718, 2020. [pdf]
L. Chizat, F. Bach. Implicit Bias of Gradient Descent for Wide Two-layer
Neural Networks Trained with the Logistic Loss. Proceedings of the
Conference on Learning Theory (COLT) [pdf] [video] [slides]
E. Berthier, F. Bach. Max-Plus Linear Approximations for Deterministic
Continuous-State Markov Decision Processes. IEEE Control
Systems Letters, 4(3):767-772, 2020. [pdf]
L. Pillaud-Vivien, F. Bach, T. Lelièvre, A. Rudi, G. Stoltz. Statistical
Estimation of the Poincaré constant and Application to Sampling Multimodal Distributions. Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS), 2020. [pdf]
R. Berthier, F. Bach, P. Gaillard. Accelerated Gossip in Networks of
Given Dimension using Jacobi Polynomial Iterations. SIAM Journal on
Mathematics of Data Science 2(1):24-47, 2020. [pdf]
D. Scieur, A. d'Aspremont, F. Bach. Regularized Nonlinear Acceleration. Mathematical
Programming, 179:47-83, 2020. [pdf]
R. M. Gower, M. Schmidt, F. Bach. P. Richtárik. Variance-Reduced
Methods for Machine Learning. Proceedings of the IEEE,
108(11):1968-1983, 2020. [pdf]
A. Dieuleveut, A. Durmus, F. Bach. Bridging the Gap between Constant Step
Size Stochastic Gradient Descent and Markov Chains. Annals of
Statistics, 48(3):1348-1382, 2020. [pdf]
2019
P. Askenazy, F. Bach. IA et emploi : Une menace artificielle. Pouvoirs, 170, 33-41,
2019. [pdf]
K. Scaman, F. Bach, S. Bubeck, Y.-T. Lee, L.
Massoulié. Optimal Convergence Rates for Convex Distributed Optimization
in Networks. Journal of Machine Learning Research, 20(159):1-31,
2019. [pdf]
U. Marteau-Ferey, F. Bach, A. Rudi. Globally convergent Newton methods for
ill-conditioned generalized self-concordant Losses. Advances in Neural
Information Processing Systems (NeurIPS), 2019. [pdf] [supplement] [slides] [poster]
H. Hendrikx, F. Bach, L. Massoulié. An accelerated decentralized stochastic
proximal algorithm for finite Sums. Advances in Neural Information
Processing Systems (NeurIPS), 2019. [pdf] [supplement]
L. Chizat, E. Oyallon, F. Bach. On Lazy Training in Differentiable Programming. Advances
in Neural Information Processing Systems (NeurIPS), 2019. [pdf] [supplement] [poster]
C. Ciliberto, F. Bach, A. Rudi. Localized
Structured Prediction. Advances in Neural Information Processing Systems
(NeurIPS), 2019. [pdf] [supplement]
J. Altschuler, F. Bach, A. Rudi, J. Niles-Weed. Massively scalable
Sinkhorn distances via the Nyström method. Advances in Neural
Information Processing Systems (NeurIPS), 2019. [pdf]
[supplement]
T. Ryffel, E. Dufour Sans, R. Gay, F. Bach, D. Pointcheval. Partially Encrypted Machine Learning using Functional Encryption. Advances in Neural Information Processing
Systems (NeurIPS), 2019. [pdf] [supplement]
K. S. Sesh Kumar, F. Bach, T. Pock. Fast Decomposable Submodular Function
Minimization using Constrained Total Variation. Advances in Neural
Information Processing Systems (NeurIPS), 2019. [pdf] [supplement]
G. Gidel, F. Bach and S. Lacoste-Julien. Implicit Regularization of Discrete Gradient Dynamics in
Linear Neural Networks. Advances in Neural Information
Processing Systems (NeurIPS), 2019. [pdf] [supplement]
O. Sebbouh, N. Gazagnadou, S. Jelassi, F. Bach,
R. Gower. Towards closing the gap between the theory and practice
of SVRG. Advances in Neural
Information Processing Systems (NeurIPS),
2019. [pdf] [supplement]
A. Kavis, K. Y. Levy, F. Bach, V.
Cevher. UniXGrad: A Universal, Adaptive Algorithm with Optimal
Guarantees for Constrained Optimization. Advances
in Neural Information Processing Systems (NeurIPS), 2019. [pdf] [supplement]
F. Bach. Max-plus matching
pursuit for deterministic Markov decision processes. Technical report, arXiv-1906.08524, 2019. [pdf]
T. Shpakova, F. Bach, M. E. Davies. Hyper-parameter
Learning for Sparse Structured Probabilistic Models. Proceedings of the International Conference on Acoustics,
Speech, and Signal Processing (ICASSP),
2019. [pdf]
D. Babichev, D. Ostrovskii, F. Bach. Efficient
Primal-Dual Algorithms for Large-Scale Multiclass Classification. Technical
report, arXiv-1902.03755, 2019. [pdf]
U. Marteau-Ferey, D. Ostrovskii, F. Bach, A.
Rudi. Beyond Least-Squares: Fast Rates for Regularized Empirical Risk
Minimization through Self-Concordance. Proceedings of the International
Conference on Learning Theory (COLT), 2019. [pdf] [poster] [slides] [video]
F. Bach, K. Y. Levy. A Universal Algorithm for Variational Inequalities
Adaptive to Smoothness and Noise. Proceedings of the International
Conference on Learning Theory (COLT), 2019. [pdf]
A. Taylor, F. Bach. Stochastic first-order
methods: non-asymptotic and computer-aided analyses via potential functions. Proceedings
of the International Conference on Learning Theory (COLT), 2019. [pdf] [code] [slides] [video]
A. Nowak-Vila, F. Bach, A. Rudi. A
General Theory for Structured Prediction with Smooth Convex Surrogates. Technical
report, arXiv-1902.01958, 2019. [pdf]
H. V. Vo, F. Bach, M. Cho, K. Han, Y. Le
Cun, P. Perez, J. Ponce. Unsupervised Image Matching and Object
Discovery as Optimization. Proceedings of the Conference on
Computer Vision and Pattern Recognition (CVPR), 2019. [pdf]
H. Hendrikx, L. Massoulié, F. Bach. Accelerated Decentralized
Optimization with Local Updates for Smooth and Strongly Convex Objectives. Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS), 2019. [pdf]
A. Nowak-Vila, F. Bach, A. Rudi. Sharp Analysis of Learning with
Discrete Losses. Proceedings of the International Conference on
Artificial Intelligence and Statistics (AISTATS), 2019. [pdf]
S. Vaswani, F. Bach, M. Schmidt. Fast and Faster Convergence of SGD for
Over-Parameterized Models and an Accelerated Perceptron. Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS), 2019. [pdf]
A. Genevay, L. Chizat, F. Bach, M. Cuturi, G. Peyré. Sample Complexity of
Sinkhorn divergences. Proceedings of the International Conference
on Artificial Intelligence and Statistics (AISTATS), 2019. [pdf] [supplement]
P. Ablin, A. Gramfort, J.-F. Cardoso, F. Bach. Stochastic algorithms with
descent guarantees for ICA. Proceedings of the International Conference
on Artificial Intelligence and Statistics (AISTATS), 2019. [pdf]
A. Podosinnikova, A. Perry, A. Wein, F. Bach, A. d'Aspremont, D. Sontag. Overcomplete
Independent Component Analysis via SDP. Proceedings of the International
Conference on Artificial Intelligence and Statistics (AISTATS), 2019. [pdf]
F. Bach. Submodular Functions: from Discrete to Continuous Domains. Mathematical
Programming, 175(1), 419-459, 2019. [pdf] [code] [slides]
L. Rencker, F. Bach, W. Wang, M. D. Plumbley. Sparse Recovery and Dictionary
Learning From Nonlinear Compressive Measurements. IEEE Transactions
in Signal Processing, 67(21):5659-5670, 2019. [pdf]
2018
L. Pillaud-Vivien, A. Rudi, F. Bach. Statistical
Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple
Passes. Advances in Neural
Information Processing Systems (NIPS), 2018.
[pdf] [supplement][slides]
L. Chizat, F. Bach. On the Global Convergence
of Gradient Descent for Over-parameterized Models using Optimal Transport. Advances
in Neural Information Processing Systems (NeurIPS), 2018. [pdf] [supplement] [poster]
K. Scaman, F. Bach, S. Bubeck, Y.-T. Lee, L. Massoulié. Optimal Algorithms
for Non-Smooth Distributed Optimization in Networks. Advances in
Neural Information Processing Systems (NeurIPS), 2018. [pdf] [supplement]
A. Defossez, N. Zeghidour, N. Usunier, L.
Bottou, F. Bach. SING: Symbol-to-Instrument Neural Generator. Advances
in Neural Information Processing Systems (NeurIPS), 2018. [pdf] [audio samples]
F. Bach. Efficient Algorithms for
Non-convex Isotonic Regression through Submodular Optimization. Advances
in Neural Information Processing Systems (NeurIPS), 2018. [pdf] [supplement]
J. Tang, M. Golbabaee, F. Bach, M. E. Davies. Rest-Katyusha:
Exploiting the Solution's Structure via Scheduled Restart Schemes. Advances
in Neural Information Processing Systems (NeurIPS), 2018. [pdf] [supplement]
E. Pauwels, F. Bach, J.-P. Vert. Relating Leverage Scores and Density
using Regularized Christoffel Functions. Advances in Neural
Information Processing Systems (NeurIPS), 2018. [pdf] [supplement]
D. Scieur, E. Oyallon, A. d'Aspremont, F. Bach. Nonlinear Acceleration of
Deep Neural Networks. Technical report, arXiv-1805.09639, 2018. [pdf]
D. Babichev and F. Bach. Constant Step
Size Stochastic Gradient Descent for Probabilistic Modeling. Proceedings
of the conference on Uncertainty in Artificial Intelligence (UAI), 2018.
[pdf]
T. Shpakova, F. Bach and A. Osokin. Marginal Weighted Maximum Log-likelihood
for Efficient Learning of Perturb-and-Map models. Proceedings of
the conference on Uncertainty in Artificial Intelligence (UAI), 2018.
[pdf]
L. Rencker, F. Bach, W. Wang, M. D. Plumbley. Consistent dictionary learning
for signal declipping. International Conference on Latent Variable
Analysis and Signal Separation, 2018. [pdf]
L. Pillaud-Vivien, A. Rudi, F. Bach. Exponential convergence of testing
error for stochastic gradient methods. Proceedings of the International
Conference on Learning Theory (COLT), 2018. [pdf]
N. Tripuraneni, N. Flammarion, F. Bach, M. I. Jordan. Averaging Stochastic
Gradient Descent on Riemannian Manifolds. Proceedings of the
International Conference on Learning Theory (COLT), 2018. [pdf]
D. Babichev and F. Bach. Slice inverse regression with score functions. Electronic Journal of Statistics, 12(1):1507-1543, 2018. [pdf]
R. M. Gower, N. Le Roux, F. Bach. Tracking the gradients using the
Hessian: A new look at variance reducing stochastic methods. Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS), 2018. [pdf] [code]
A. Kundu, F. Bach, C. Bhattacharyya. Convex optimization over
intersection of simple sets: improved convergence rate guarantees via an exact
penalty approach. Proceedings of the International Conference on
Artificial Intelligence and Statistics (AISTATS), 2018. [pdf]
S. Reddi, M. Zaheer, S. Sra, B. Poczos, F. Bach, R. Salakhutdinov, A.
Smola. A Generic Approach for Escaping Saddle points. Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS), 2018. [pdf]
M. El Halabi, F. Bach, V. Cevher. Combinatorial
Penalties: Which structures are preserved by convex relaxations? Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS), 2018. [pdf]
C. Dupuy and F. Bach. Learning
Determinantal Point Processes in Sublinear Time. Proceedings of the
International Conference on Artificial Intelligence and Statistics (AISTATS),
2018. [pdf]
T. Schatz, F. Bach, E. Dupoux. Evaluating automatic speech recognition systems
as quantitative models of cross-lingual phonetic category perception. Journal
of the Acoustical Society of America, Express Letters, 3(55), 2018. [pdf]
2017
A. Defossez, F. Bach. AdaBatch:
Efficient Gradient Aggregation Rules for Sequential and Parallel Stochastic Gradient
Methods. Technical Report, Arxiv-1711.01761,
2017. [pdf]
J. Weed, F. Bach. Sharp asymptotic and finite-sample rates of convergence
of empirical measures in Wasserstein distance. Technical Report,
Arxiv-1707.00087, 2017. To appear in Bernoulli. [pdf]
D. Scieur, A. d'Aspremont, F. Bach. Nonlinear
Acceleration of Stochastic Algorithms. Advances in Neural Information
Processing Systems (NIPS), 2017. [pdf]
A. Osokin, F. Bach, S. Lacoste-Julien. On Structured Prediction Theory
with Calibrated Convex Surrogate Losses. Advances in Neural
Information Processing Systems (NIPS), 2017. [pdf]
D. Scieur, V. Roulet, F. Bach, A. d'Aspremont. Integration Methods and
Accelerated Optimization Algorithms. Advances in Neural Information
Processing Systems (NIPS), 2017. [pdf]
A. Dieuleveut, N. Flammarion, and F. Bach. Harder,
Better, Faster, Stronger Convergence Rates for Least-Squares Regression. Journal
of Machine Learning Research, 18(101):1?51, 2017. [pdf]
N. Flammarion, P. Balamurugan, F.
Bach. Robust Discriminative Clustering with Sparse Regularizers. Journal
of Machine Learning Research, 18(80):1-50, 2017. [pdf]
F. Pedregosa, F. Bach, A. Gramfort. On the Consistency of Ordinal Regression
Methods. Journal of Machine Learning Research, 18(55):1-35,
2017. [pdf]
F. Bach. On the Equivalence between Kernel Quadrature Rules and Random
Feature Expansions. Journal of Machine Learning Research,
18(19):1-38, 2017. [pdf]
F. Bach. Breaking the Curse of
Dimensionality with Convex Neural Networks. Journal of Machine
Learning Research, 18(19):1-53, 2017. [pdf]
K. Scaman, F. Bach, S. Bubeck, Y.-T. Lee, L.
Massoulié. Optimal algorithms for smooth and strongly convex distributed
optimization in networks. Proceedings of the International
Conference on Machine Learning (ICML), 2017. [pdf]
N. Flammarion, F.
Bach. Stochastic Composite Least-Squares Regression with convergence
rate O(1/n). Proceedings of the International Conference on
Learning Theory (COLT), 2017. [pdf]
R. Rezende, J. Zepeda, J.
Ponce, F. Bach, P. Pérez. Kernel square-loss exemplar machines for image
retrieval. Proceedings of the
Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [pdf] [code]
R. Sankaran, F. Bach, C. Bhattacharyya. Identifying groups of strongly
correlated variables through Smoothed Ordered Weighted L1-norms. Proceedings of the International Conference on Artificial
Intelligence and Statistics (AISTATS), 2017. [pdf]
C. Dupuy and F. Bach. Online but Accurate Inference for Latent Variable
Models with Local Gibbs Sampling. Journal of Machine Learning
Research, 18(126):1?45, 2017. [pdf] [code]
K. S. Sesh Kumar, F. Bach. Active-set
Methods for Submodular Minimization Problems. Journal of Machine Learning
Research, 18(132):1?31, 2017. [pdf]
F. Yanez, F. Bach. Primal-Dual
Algorithms for Non-negative Matrix Factorization with the Kullback-Leibler
Divergence. Proceedings of the International Conference on
Acoustics, Speech, and Signal Processing (ICASSP), 2017. [pdf] [code]
T. Schatz, R. Turnbull, F. Bach, E. Dupoux. A Quantitative Measure of the
Impact of Coarticulation on Phone Discriminability. Proceedings of
INTERSPEECH, 2017. [pdf]
2016
G. Obozinski and F. Bach. A unified perspective on convex structured sparsity:
Hierarchical, symmetric, submodular norms and beyond. Technical report,
HAL-01412385, 2016. [pdf]
T. Shpakova and F. Bach. Parameter
Learning for Log-supermodular Distributions. Advances in Neural Information
Processing Systems (NIPS). [pdf]
D. Scieur, A. d'Aspremont, F. Bach. Regularized
Nonlinear Acceleration. Advances in Neural Information Processing Systems
(NIPS). [pdf]
P. Balamurugan and F. Bach. Stochastic
Variance Reduction Methods for Saddle-Point Problems. Advances in
Neural Information Processing Systems (NIPS). [pdf] [code]
A. Genevay, M. Cuturi, G. Peyré, F. Bach. Stochastic Optimization for
Large-scale Optimal Transport. Advances in Neural Information
Processing Systems (NIPS). [pdf]
P. Germain, F. Bach, S.
Lacoste-Julien. PAC-Bayesian Theory Meets Bayesian Inference. Advances
in Neural Information Processing Systems (NIPS), 2016. [pdf]
M. Schmidt, N. Le Roux, F. Bach. Minimizing
Finite Sums with the Stochastic Average Gradient. Mathematical
Programming, 162(1):83-112, 2016. [pdf] [code]
A. Dieuleveut, F. Bach. Non-parametric
Stochastic Approximation with Large Step sizes. The Annals of Statistics,
44(4):1363-1399, 2016. [pdf]
F. Bach, V. Perchet. Highly-Smooth Zero-th Order Online Optimization. Proceedings
of the Conference on Learning Theory (COLT), 2016. [pdf]
A. Podosinnikova, F. Bach, S. Lacoste-Julien. Beyond CCA: Moment Matching
for Multi-View Models. Proceedings of the International Conference
on Machine Learning (ICML), 2016. [pdf]
R. Lajugie, P. Bojanowski, P. Cuvillier, S. Arlot, F. Bach. A weakly-supervised
discriminative model for audio-to-score alignment. Proceedings of
the International Conference on Acoustics, Speech, and Signal Processing (ICASSP),
2016. [pdf]
2015
A. Podosinnikova, F. Bach, S. Lacoste-Julien. Rethinking LDA: moment matching
for discrete ICA. Advances in Neural Information Processing Systems
(NIPS), 2015. [pdf]
R. Shivanna, B. Chatterjee, R. Sankaran, C. Bhattacharyya, F. Bach. Spectral
Norm Regularization of Orthonormal Representations for Graph Transduction. Advances
in Neural Information Processing Systems (NIPS), 2015. [pdf]
P. Bojanowski, R. Lajugie, E. Grave, F. Bach,
I. Laptev, J. Ponce and C. Schmid. Weakly-Supervised Alignment of Video
With Text. Proceedings of the International Conference on Computer
Vision (ICCV), 2015. [pdf]
V. Roulet, F. Fogel, A. d'Aspremont, F. Bach. Supervised Clustering in the
Data Cube. Technical Report, ArXiv 1506.04908, 2015. [pdf]
F. Fogel, R. Jenatton, F. Bach, A. d'Aspremont. Convex Relaxations
for Permutation Problems. SIAM Journal on Matrix Analysis and
Application, 36(4):1465-1488, 2015. [pdf]
N. Flammarion, F. Bach. From Averaging to Acceleration, There is Only a
Step-size. Proceedings of the International Conference on Learning Theory
(COLT), 2015. [pdf]
R. Lajugie, P. Bojanowski, S. Arlot and F. Bach. Semidefinite and Spectral
Relaxations for Multi-Label Classification. Technical report, HAL- 01159321,
2015. [pdf]
K. S. Sesh Kumar, A. Barbero, S. Jegelka, S. Sra, F. Bach. Convex Optimization
for Parallel Energy Minimization. Technical report, HAL-01123492, 2015. [pdf]
N. Shervashidze and F. Bach. Learning the Structure for Structured Sparsity. IEEE
Transactions on Signal Processing, 63(18):4894-4902. [pdf] [code]
A. Defossez, F. Bach. Averaged Least-Mean-Square: Bias-Variance Trade-offs
and Optimal Sampling Distributions. Proceedings of the International
Conference on Artificial Intelligence and Statistics (AISTATS), 2015. [pdf]
S. Lacoste-Julien, F. Lindsten, F. Bach. Sequential Kernel Herding: Frank-Wolfe
Optimization for Particle Filtering. Proceedings of the International
Conference on Artificial Intelligence and Statistics (AISTATS), 2015. [pdf]
A. Bietti, F. Bach, A. Cont. An online EM algorithm in hidden (semi-)Markov
models for audio segmentation and clustering. Proceedings of the
International Conference on Acoustics, Speech, and Signal Processing (ICASSP),
2015. [pdf]
F. Bach. Duality between subgradient and conditional gradient methods. SIAM
Journal of Optimization, 25(1):115-129, 2015. [pdf]
R. Gribonval, R. Jenatton, F. Bach, M. Kleinsteuber, M. Seibert. Sample
Complexity of Dictionary Learning and Other Matrix Factorizations. IEEE
Transactions on Information Theory, 61(6):3469-3486, 2015. [pdf]
R. Gribonval, R. Jenatton, F. Bach. Sparse
and spurious: dictionary learning with noise and outliers. IEEE Transactions on Information Theory, 61(11):
6298-6319, 2015. [pdf]
2014
J. Mairal, F. Bach, J. Ponce. Sparse Modeling for Image and Vision Processing. Foundations and Trends in Computer Vision, 8(2-3):85-283, 2014. [pdf]
D. Garreau, R. Lajugie, S. Arlot and F.
Bach. Metric Learning for Temporal Sequence Alignment. Advances in Neural Information
Processing Systems (NIPS), 2014. [pdf]
A. Defazio, F. Bach, S. Lacoste-Julien. SAGA:
A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite
Objectives. Advances in
Neural Information Processing Systems (NIPS),
2014. [pdf]
P. Bojanowski, R. Lajugie, F. Bach, I. Laptev,
J. Ponce, C. Schmid and J. Sivic. Weakly-Supervised Action Labeling
in Videos Under Ordering Constraints. Proceedings
of the European Conference on Computer Vision (ECCV), 2014. [pdf]
E. Grave, G. Obozinski, F. Bach. A Markovian
approach to distributional semantics with application to semantic compositionality.
Proceedings of the International Conference on
Computational Linguistics (COLING), 2014. [pdf]
R. Lajugie, S. Arlot and F. Bach. Large-Margin Metric Learning for Partitioning Problems. Proceedings of the International Conference on Machine Learning (ICML), 2014. [pdf]
F. Bach. Adaptivity of averaged stochastic
gradient descent to local strong convexity for logistic regression. Journal
of Machine Learning Research, 15(Feb):595-627, 2014. [pdf]
A. d'Aspremont, F. Bach, L. El Ghaoui. Approximation Bounds for Sparse Principal
Component Analysis. Mathematical Programming, 2014. [pdf]
T. Schatz, V. Peddinti, X.-N. Cao, F. Bach, H. Hynek, E. Dupoux. Evaluating
Speech Features with the Minimal-Pair ABX task (II): Resistance to Noise. Proceedings
of INTERSPEECH, 2014. [pdf]
2013
F. Bach. Learning with Submodular Functions: A Convex Optimization Perspective. Foundations and Trends in Machine
Learning, 6(2-3):145-373, 2013. [FOT website] [pdf] [slides]
F. Bach and E. Moulines. Non-strongly-convex
smooth stochastic approximation with convergence rate O(1/n). Advances
in Neural Information Processing Systems (NIPS). [pdf] [slides] [IPAM slides]
S. Jegelka, F. Bach, S. Sra. Reflection
methods for user-friendly submodular optimization. Advances in Neural Information Processing Systems (NIPS). [pdf]
B. Mishra, G. Meyer, F. Bach, R.
Sepulchre. Low-rank optimization with trace norm penalty. SIAM Journal on Optimization, 23(4):2124-2149, 2013. [pdf]
F. Fogel, R. Jenatton, F. Bach, A.
d'Aspremont. Convex Relaxations for Permutation Problems. Technical report, arXiv:1306.4805, 2013. To appear
in Advances in Neural Information Processing Systems
(NIPS). [pdf]
K. S. Sesh Kumar and F. Bach. Maximizing
submodular functions using probabilistic graphical models. Technical report, HAL 00860575, 2013. [pdf]
A. Nelakanti, C. Archambeau,
J. Mairal, F. Bach, G. Bouchard. Structured Penalties for Log-linear Language Models. Proceedings of the Conference on Empirical Methods in Natural Language
Processing (EMNLP), 2013. [pdf]
P. Bojanowski, F. Bach, I. Laptev, J. Ponce,
C. Schmid and J. Sivic. Finding Actors and Actions in Movies. Proceedings of the International Conference
on Computer Vision (ICCV), 2013. [pdf]
F. Bach. Convex relaxations of
structured matrix factorizations.
Technical report, HAL 00861118, 2013. [pdf]
T. Schatz, V. Peddinti, F. Bach, A. Jansen, H.
Hynek, E. Dupoux. Evaluating speech features with the Minimal-Pair
ABX task: Analysis of the classical MFC/PLP pipeline. Proceedings of INTERSPEECH, 2013. [pdf]
Z. Harchaoui, F. Bach, O. Cappe and E.
Moulines. Kernel-Based Methods for Hypothesis Testing: A Unified
View. IEEE Signal
processing Magazine, 30(4): 87-97, 2013. [pdf]
E. Grave, G. Obozinski, F. Bach. Hidden
Markov tree models for semantic class induction. Proceedings of the Conference on Computational Natural Language Learning
(CoNLL), 2013. [pdf]
E. Richard, F. Bach, and J.-P. Vert. Intersecting
singularities for multi-structured estimation. Proceedings
of the International Conference on Machine Learning (ICML), 2013. [pdf]
G. Rigaill, T. D. Hocking, F. Bach, and J.-P. Vert. Learning
Sparse Penalties for Change-Point Detection using Max Margin Interval
Regression. Proceedings of
the International Conference on Machine Learning (ICML), 2013. [pdf]
K. S. Sesh Kumar and F. Bach. Convex relaxations for learning bounded-treewidth
decomposable graphs. Proceedings of
the International Conference on Machine Learning (ICML), 2013. [pdf]
T. D. Hocking, G. Schleiermacher, I. Janoueix-Lerosey, O. Delattre, F. Bach, J.-P.
Vert. Learning smoothing models of copy number profiles using breakpoint
annotations. BMC Bioinformatics, 14:1-15, 2013. [pdf]
F. Bach. Sharp
analysis of low-rank kernel matrix approximations. Technical report, HAL
00723365. Proceedings of the International Conference on Learning Theory
(COLT), 2013. [pdf]
N.
Le Roux, F. Bach. Local component analysis. Proceedings of the
International Conference on Learning Representations (ICLR), 2013. [pdf]
2012
S. Lacoste-Julien, M. Schmidt, F.
Bach. A Simpler Approach to Obtaining an O(1/t) Convergence Rate for
the Projected Stochastic Subgradient Method. Technical report
arXiv:1212.2002v2, December 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]
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. [pdf] [slides]
H. Kadri, A. Rakotomamonjy, F. Bach, P. Preux. Multiple Operator-valued
Kernel Learning. Advances in Neural Information Processing Systems (NIPS). Technical
report, HAL 00677012, 2012. [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. 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. [pdf]
A. Joulin, F. Bach, J. Ponce. Multi-Class Cosegmentation. Proceedings
of the Conference on Computer Vision and Pattern Recognition (CVPR), 2012. [pdf]
F. Bach, R. Jenatton, J. Mairal, G. Obozinski. Structured sparsity through
convex optimization. Statistical Science, 27(4):450-468, 2012.
[pdf] [slides]
F. Bach, R. Jenatton, J. Mairal, G. Obozinski. Optimization with
sparsity-inducing penalties. Foundations and Trends in Machine
Learning, 4(1):1-106, 2012. [FOT website] [pdf] [slides]
J. Mairal, F. Bach, J. Ponce. Task-Driven Dictionary Learning. IEEE
Transactions on Pattern Analysis and Machine Intelligence, 34(4):791-804, 2012. [pdf]
2011
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] [long-version-pdf-HAL]
E. Grave, G. Obozinski, F. Bach. Trace Lasso: a trace norm regularization
for correlated designs. Advances in Neural Information Processing
Systems (NIPS), 2011. [pdf] [long-version-pdf-HAL]
F. Bach. Shaping Level Sets with
Submodular Functions. Advances in Neural Information Processing
Systems (NIPS), 2011. [pdf] [long-version-pdf-HAL]
B. Mishra, G. Meyer, F. Bach, R. Sepulchre. Low-rank optimization with trace
norm penalty. Technical report, Arxiv 1112.2318, 2011. [pdf]
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]
J. Mairal, R. Jenatton, G. Obozinski, F. Bach. Convex and Network Flow
Optimization for Structured Sparsity. Journal of Machine Learning
Research, 12, 2681-2720. [pdf]
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. Lefevre, F. Bach, C. Fevotte. 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. Benoit, 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. Lefevre, F. Bach, C. Fevotte, 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. Journee, 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]
F. Bach. Consistency of the group
Lasso and multiple kernel learning, Journal of Machine Learning
Research, 9, 1179-1225, 2008. [pdf] [slides]
F. Bach. Consistency of trace norm minimization, Journal of
Machine Learning Research, 9, 1019-1048, 2008. [pdf]
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
Z. Harchaoui, F. Bach, and
E. Moulines. Testing for Homogeneity
with Kernel Fisher Discriminant Analysis, Advances
in Neural Information Processing Systems (NIPS) 20, 2007. [pdf] [long version, HAL-00270806, 2008]
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
the Eigth International Symposium
on Mathematical Morphology (ISMM), 2007. [pdf]
A. d'Aspremont, F. Bach and L. El
Ghaoui. Full regularization path for sparse
principal component analysis. Proceedings of the
Twenty-fourth International Conference on Machine Learning (ICML), 2007. [pdf] [tech-report, arXiv]
A. Rakotomamonjy, F. Bach, S. Canu, and Y.
Grandvalet. More Efficiency in Multiple Kernel
Learning, Proceedings of the Twenty-fourth International Conference
on Machine Learning (ICML), 2007. [pdf]
Z. Harchaoui and F. Bach. Image
classification with segmentation graph kernels, Proceedings
of the Conference on Computer Vision and Pattern Recognition (CVPR), 2007. [pdf] [presentation]
J. Louradour, K. Daoudi and F. Bach. Feature
Space Mahalanobis Sequence Kernels: Application to SVM Speaker Verification. IEEE Transactions on Audio, Speech
and Language Processing, 15 (8), 2465-2475,
2007.
Y. Yamanishi, F. Bach., and J.-P. Vert. Glycan
Classification with Tree Kernels, Bioinformatics, 23(10):1211-1216, 2007. [pdf] [web supplements]
K. Fukumizu, F. Bach, A. Gretton. Consistency
of Kernel Canonical Correlation Analysis. Journal
of Machine Learning Research, 8,
361-383, 2007. [pdf]
2006
J. Abernethy, F. Bach, T. Evgeniou, and J.-P.
Vert. Low-rank matrix factorization with attributes. Technical report N24/06/MM, Ecole des Mines de Paris,
2006. [pdf] [ArXiv]
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]
J. Louradour, K.
Daoudi, F. Bach, SVM Speaker
Verification using an Incomplete Cholesky Decomposition Sequence Kernel. Proc. Odyssey, San Juan, Porto Rico, 2006. [pdf] [slides]
2005
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. [pdf]
2003
F. Bach, M. I. Jordan. Beyond
independent components: trees and clusters, Journal of Machine
Learning Research, 4, 1205-1233, 2003. [pdf] [matlab code]
F. Bach, M. I. Jordan. Learning
spectral clustering, Advances in Neural Information Processing
Systems (NIPS) 16, 2004. [pdf] [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. [pdf] [pdf, in Japanese]
F. Bach, M. I. Jordan. Analyse en composantes
independantes et reseaux Bayesiens, 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. [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. [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. [pdf]
F. Bach, M. I. Jordan. Kernel
independent component analysis, Journal of Machine Learning
Research, 3, 1-48, 2002. [pdf] [matlab code]
F. Bach, M. I. Jordan. Tree-dependent component analysis, Uncertainty
in Artificial Intelligence (UAI): Proceedings of the Eighteenth Conference,
2002. [pdf] [matlab code]
2001
F. Bach, M. I. Jordan. Thin
junction trees, Advances in Neural Information Processing Systems
(NIPS) 14, 2002. [pdf]
Software
Minimizing Finite Sums with the Stochastic
Average Gradient
Submodular optimization (matlab)
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)
Tutorials / mini-courses (older ones)
August 2018: Machine Learning Summer School, Madrid - Large-scale machine
learning and convex optimization [slides]
September 2017: StatMathAppli 2017, Fréjus - Large-scale machine learning
and convex optimization [slides]
May 2017: SIAM
Conference on Optimization mini-tutorial on "Stochastic Variance-Reduced
Optimization for Machine Learning" [part 1] [part 2]
December 2016: NIPS 2016 Tutorial on "Large-Scale Optimization: Beyond
Stochastic Gradient Descent and Convexity" [part 1] [part 2]
July 2016: IFCAM summer
school, Indian Institute of Science, Bangalore - Large-scale machine
learning and convex optimization [slides]
May 2016: Machine Learning Summer School, Cadiz - Large-scale machine learning
and convex optimization [slides]
February 2016: Statistical learning week, CIRM, Luminy - Large-scale machine learning
and convex optimization [slides]
January 2016: Winter School on Advances in
Mathematics of Signal Processing, Bonn - Large-scale machine learning and convex optimization [slides]
July 2014: IFCAM Summer School, Indian Institute of Science, Bangalore
- Large-scale machine learning and convex optimization [slides]
March 2014: YES Workshop, Eurandom, Eindhoven - Large-scale
machine learning and convex optimization [slides]
September 2013: Fourth Cargese Workshop on Combinatorial Optimization - Machine learning and convex
optimization with submodular functions
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)
Courses (older ones)
Fall 2020: Learning theory from first
principles - Mastere M2 Mash
Spring 2020: Optimisation et Apprentissage Statistique - Master
M2 "Mathematiques de l'aleatoire" - Universite Paris-Sud (Orsay)
Spring 2020: Machine Learning - Masters ICFP, Ecole Normale
Superieure
Fall 2018: Statistical machine learning - Master M1 -
Ecole Normale Superieure (Paris)
Fall 2018: Statistical machine learning - Master M1 -
Ecole Normale Superieure (Paris)
Spring 2018: Optimisation et Apprentissage
Statistique - Master M2 "Mathematiques de l'aleatoire" - Universite
Paris-Sud (Orsay)
Fall 2017: Statistical machine learning - Master M1 -
Ecole Normale Superieure (Paris)
Spring 2017: Optimisation et Apprentissage
Statistique - Master M2 "Mathematiques de l'aleatoire" - Universite
Paris-Sud (Orsay)
Spring 2016: Optimisation et Apprentissage
Statistique - Master M2 "Mathematiques de l'aleatoire" - Universite
Paris-Sud (Orsay)
Fall 2014: Statistical machine learning -
Master M1 - Ecole Normale Superieure (Paris)
Fall 2014: An introduction to graphical models
- Master M2 "Mathematiques, Vision, Apprentissage" - Ecole Normale
Superieure de Cachan
Spring 2014: Statistical machine learning -
Master M2 "Probabilites et Statistiques" - Universite Paris-Sud
(Orsay)
Spring 2013: Statistical machine learning - Master
M2 "Probabilites et Statistiques" - Universite Paris-Sud (Orsay)
Spring 2013: Statistical machine learning - Filiere
Math/Info - L3 - Ecole Normale Superieure (Paris)
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
"Mathematiques, Vision, Apprentissage" - Ecole Normale Superieure de
Cachan
Spring 2011: Statistical machine learning - Master M2 "Probabilites
et Statistiques" - Universite Paris-Sud (Orsay)
Fall 2010: An introduction to graphical models - Master M2
"Mathematiques, Vision,Apprentissage" - Ecole Normale Superieure de
Cachan
Spring 2010: Statistical machine learning
- Master M2 "Probabilites et Statistiques" - Universite Paris-Sud (Orsay)
Fall 2009: An
introduction to graphical models - Master M2 "Mathematiques,
Vision,Apprentissage" - Ecole Normale Superieure de Cachan
Fall 2008: An
introduction to graphical models - Master M2 "Mathematiques, Vision, Apprentissage"
- Ecole Normale Superieure de Cachan
May 2008: Probabilistic modelling
and graphical models: Enseignement Specialise - Ecole des Mines de Paris
Fall 2007: An
introduction to graphical models - Master M2 "Mathematiques, Vision, Apprentissage"
- Ecole Normale Superieure de Cachan
May 2007: Probabilistic modelling
and graphical models: Enseignement Specialise - Ecole des Mines de Paris
Fall 2006: An introduction to
graphical models - Master M2 "Mathematiques, Vision, Apprentissage" -
Ecole Normale Superieure de Cachan
Fall 2005: An introduction to
graphical models - Master M2 "Mathematiques, Vision, Apprentissage" -
Ecole Normale Superieure de Cachan