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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, to appear in Fall 2024 at MIT Press
Final draft
Code (python, Matlab)
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
Bertille Follain
Alexandre François, co-advised with Antonio
Orvieto
David
Holzmüller
Etienne Gauthier, co-advised with Michael Jordan
Frederik Kunstner
Marc Lambert, co-advised with Silvère Bonnabel
Ivan Lerner, co-advised with Anita Burgun
and Antoine Neuraz
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, Postdoctoral Researcher,
Ecole Polytechnique Federale de Lausanne
Alberto Bietti, Postdoctoral Researcher, New York
University
Vivien Cabannes, Postdoctoral Researcher, Facebook,
New York
Lénaïc Chizat, Assistant Professor, EPFL, Ecole
Polytechnique Fédérale de Lausanne
Timothee Cour,
Engineer at Google
Hadi Daneshmand, Post-doctoral fellow, Princeton
University
Alexandre Défossez,
Research scientist, Kyutai, Paris
Aymeric Dieuleveut, Professor, Ecole
Polytechnique, Palaiseau
Christophe Dupuy,
Amazon, Cambridge, USA
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, Facebook AI Research, 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
Augustin Lefèvre, Data scientist, YKems
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
2024
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. Technical report, arXiv: 2408.16543,
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. Technical report, arXiv: 2404.14806,
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]
2023
B. Follain, F. Bach. Nonparametric Linear Feature Learning in
Regression Through Regularisation. Technical report, arXiv:2307.12754,
2023. [pdf]
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