Francis Bach INRIA - SIERRA project-team PSL Research University CS61534 75647 Paris Cedex |
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)
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]
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]
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)
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
PhD Students and Postdocs
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
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]
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]
2024
E.
Beyler, F. Bach. Variational Inference on the
Boolean Hypercube with the Quantum Entropy. Technical report,
arXiv:2411.03759, 2024. [pdf]
A.
H. Ribeiro, T. B. Schön, D. Zachariah, F. Bach. Efficient Optimization
Algorithms for Linear Adversarial Training. Technical report,
arXiv:2410.12677, 2024. [pdf]
S. G. Gruber, F. Bach. Optimizing Estimators of Squared Calibration Errors
in Classification. Technical report, arXiv:2410.07014, 2024. [pdf]
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)
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 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)ematiques, Vision, Apprentissage" - Ecole Normale
Superieure de Cachan
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)ematiques, Vision, Apprentissage" - Ecole Normale
Superieure de Cachan
Spring 2016: Optimisation et Apprentissage Statistique - Master M2 "Mathematiques de l'aleatoire" - Universite Paris-Sud (Orsay)ematiques, Vision, Apprentissage" - Ecole Normale
Superieure de Cachan
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)ematiques, Vision, Apprentissage" - Ecole
Normale Superieure de Cachan
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