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Francis Bach INRIA - SIERRA
project-team PSL Research University CS61534 75647 Paris Cedex
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Blog - Tutorials - Courses - Students - Alumni - Publications - Software
Book: Learning Theory from First
Principles, published
in December 2024 at MIT Press
Final pdf version
Code (python, Matlab)
Blog post
I am
a researcher at INRIA, leading since 2011 the SIERRA project-team, which is part of the Computer Science Department at Ecole Normale Supérieure, and a joint team between CNRS, ENS and INRIA. I completed my Ph.D. in
Computer Science at U.C. Berkeley, working with Professor Michael Jordan, and spent two years in the Mathematical Morphology group at Ecole des Mines de Paris, I then joined the WILLOW project-team at INRIA/Ecole
Normale Supérieure/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
/ keynotes (recent - older ones below)
December 2025: International
Conference on Statistics and Data Science (ICSDS) [slides]
July 2025: Conference on Learning Theory [slides]
April 2025: Graduate
School in Systems, Optimization, Control and Networks (SOCN) [board-1] [board-2]
[board-3] [board-4]
[board-5] [board-6]
[exercises]
September 2024: CIME
School on High-Dimensional Approximation [notes]
September 2023: Summer
school on distributed learning [slides]
July 2021: PRAIRIE/MIAI AI summer school [slides]
September 2020: Hausdorff School,
MCMC: Recent developments and new connections - Large-scale machine learning
and convex optimization [slides]
June 2020: Machine Learning Summer School, Tubingen - Large-scale
machine learning and convex optimization [slides]
Courses (recent - older
ones below)
Fall 2025: Learning theory from first principles - Mastere
M2 IASD
Fall 2024: Learning theory from first principles - Mastere
M2 IASD
Fall 2023: Learning theory from first principles - Mastere
M2 Mash
Fall 2022: Learning theory from first principles - Mastere
M2 Mash
Spring 2022: Optimisation et Apprentissage
Statistique - Master M2 "Mathematiques
de l'aleatoire" - Universite Paris-Sud (Orsay)
Fall 2021: Learning theory from first principles - Mastere
M2 Mash
Spring 2021: Statistical machine learning - Ecole Normale Superieure (Paris)
Spring 2021: Machine Learning - Masters ICFP, Ecole
Normale Superieure
Spring 2021: Optimisation et Apprentissage
Statistique - Master M2 "Mathematiques de l'aleatoire" - Universite Paris-Sud (Orsay)
PhD Students and Postdocs
Eliot Beyler
Eugène
Berta, co-advised with Michael
Jordan
Nabil Boukir, co-advised with Michael Jordan
Sacha Braun, co-advised with Michael Jordan
Léo Dana, co-advised with Loucas Pillaud-Vivien
Juliette
Decugis, co-advised with Gabriel Synnaeve and Taco
Cohen
Etienne Gauthier, co-advised with Michael Jordan
Armand Gissler
Frederik Kunstner
Simon Martin, co-advised with Giulio Biroli
Fabian Schaipp,
co-advised with Umut Simsekli and Adrien Taylor
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 Brossolet (Follain), Agregio Solution
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
David
Holzmüller, Researcher, Inria,
Saclay
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, 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, Praticien Hospitalo-Universitaire, Université Paris-Cité
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
Corbinian Schlosser
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
Lawrence
Stewart, Research scientist, Google Deepmind, Paris
Adrien Taylor, Research scientist, Inria Paris
Blake Woodworth, Assistant Professor, George
Washington University
Mikhail Zaslavskiy, Byopt
Publications
2025
Sacha Braun, David Holzmüller,
Michael I. Jordan, Francis Bach. Conditional
Coverage Diagnostics for Conformal Prediction. Technical report, arXiv:2512.11779, 2025. [pdf]
Eugène Berta, David Holzmüller, Michael I. Jordan,
Francis Bach. Structured Matrix Scaling
for Multi-Class Calibration. Technical
report, arXiv:2511.03685, 2025. [pdf]
Antônio H. Ribeiro, David Vävinggren, Dave Zachariah,
Thomas B. Schön, Francis Bach. Kernel Learning with Adversarial Features: Numerical
Efficiency and Adaptive Regularization. Technical report, arXiv:2510.20883,
2025. [pdf]
Etienne Gauthier, Francis Bach, Michael I. Jordan. Adaptive Coverage
Policies in Conformal Prediction. Technical report, arXiv: 2510.04318, 2025. [pdf]
Nathan Doumèche, Francis Bach, Gérard Biau, Claire Boyer. Fast kernel methods: Sobolev,
physics-informed, and additive models. Technical report,
arXiv:2509.02649, 2025. [pdf]
Eliot Beyler, Francis Bach. Convergence of Deterministic and
Stochastic Diffusion-Model Samplers: A Simple Analysis in Wasserstein Distance.
Technical report, arXiv:2508.03210,
2025. [pdf]
Sacha Braun, Eugène Berta, Michael I.
Jordan, Francis Bach. Multivariate
Conformal Prediction via Conformalized Gaussian
Scoring. Technical report, arXiv:2507.20941,
2025. [pdf]
Francis Bach. On the Effectiveness of the z-Transform Method in Quadratic Optimization.
Technical report, arXiv:2507.03404, 2025. [pdf]
Zijian Guo, Zhenyu Wang, Yifan Hu, Francis Bach. Statistical Inference for Conditional
Group Distributionally Robust Optimization with Cross-Entropy Loss. Technical
report, arXiv:2507.09905, 2025. [pdf]
Frederik Kunstner, Francis Bach. Scaling Laws for Gradient
Descent and Sign Descent for Linear Bigram Models under Zipf’s Law.
Technical report, arXiv:2505.19227, 2025. [pdf]
Etienne Gauthier, Francis Bach, Michael I. Jordan. Backward Conformal
Prediction. Technical report, arXiv:2505.13732, 2025. [pdf]
Zhenyu Wang, Molei Liu, Jing Lei, Francis
Bach, Zijian Guo. StablePCA: Learning Shared Representations across Multiple
Sources via Minimax Optimization. Technical report, arXiv:2505.00940, 2025.
[pdf]
Marc Lambert, Francis Bach, Silvère Bonnabel. Entropy Regularized Variational Dynamic Programming
for Stochastic Optimal Control. Technical report, HAL:05016406, 2025. [pdf]
Sacha Braun, Liviu Aolaritei, Michael I. Jordan,
Francis Bach. Minimum Volume Conformal Sets for Multivariate Regression.
Technical report, arXiv:2503.19068, 2025. [pdf]
Etienne Gauthier, Francis Bach, Michael I. Jordan. E-Values Expand the
Scope of Conformal Prediction. Technical report, arXiv:2503.13050, 2025. [pdf]
Eliot Beyler, Francis Bach. Optimal Denoising in Score-Based
Generative Models: The Role of Data Regularity. Technical report,
arXiv:2503.12966, 2025. [pdf]
Léo Dana, Francis Bach, Loucas Pillaud-Vivien.
Convergence of Shallow ReLU Networks on Weakly
Interacting Data. Technical report, arXiv:2502.16977, 2025. [pdf]
Nathan Doumèche, Francis Bach, Éloi Bedek, Gérard Biau, Claire Boyer,
Yannig Goude. Forecasting time series with constraints. Technical
report, arXiv:2502.10485, 2025. [pdf]
Lawrence Stewart, Francis Bach, Quentin Berthet. Building Bridges
between Regression, Clustering, and Classification. Technical report,
arXiv:2502.02996, 2025. [pdf]
Eugène Berta, David Holzmüller, Michael I. Jordan,
Francis Bach. Rethinking Early Stopping: Refine, Then Calibrate. Technical
report, arXiv:2501.19195, 2025. [pdf]
[slides]
Alexandre François, Antonio Orvieto, Francis Bach. An Uncertainty Principle
for Linear Recurrent Neural Networks. Proceedings of the Conference on
Learning Theory (COLT), 2025. [pdf]
Francis Bach, Saeed Saremi. Sampling Binary Data by Denoising through
Score Functions. Proceedings of the International Conference on Machine
Learning (ICML), 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. Proceedings of the International
Conference on Machine Learning (ICML), 2025. [pdf]
Etienne Gauthier, Francis Bach, Michael
I. Jordan. Statistical Collusion by Collectives on
Learning Platforms. Proceedings
of the International Conference on Machine Learning (ICML), 2025. [pdf]
Sebastian G. Gruber, Francis Bach. Optimizing Estimators of Squared Calibration
Errors in Classification. Transactions of Machine Learning Research,
2025. [pdf]
Eliot Beyler, Francis Bach. Variational Inference on the Boolean
Hypercube with the Quantum Entropy. Proceedings of the International
Conference on Artificial Intelligence and Statistics (AISTATS), 2025. [pdf]
Antônio H. Ribeiro, Thomas B. Schön, Dave Zachariah, Francis Bach. Efficient
Optimization Algorithms for Linear Adversarial Training. Proceedings of the
International Conference on Artificial Intelligence and Statistics (AISTATS),
2025. [pdf]
Alessandro Rudi, Ulysse Marteau-Ferey, Francis Bach. Finding
Global Minima via Kernel Approximations. Mathematical Programming,
209(1):703-784, 2025. [pdf]
Nathan Doumèche, Francis Bach, Gérard Biau, Claire Boyer. Physics-informed kernel learning.
Journal of Machine Learning Research, 26(124):1−39, 2025. [pdf]
Bertille Follain, Francis Bach. Enhanced
Feature Learning via Regularisation: Integrating
Neural Networks and Kernel Methods. Journal of Machine Learning Research,
26(172):1-56, 2025. [pdf]
2024
C. Moucer, A. Taylor, F. Bach. Constructive approaches
to concentration inequalities with independent random variables. Technical
report, arXiv:2408.16480, 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]
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]
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]
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. Vacher, B. Muzellec, F. Bach, F.-X. Vialard, A.
Rudi. Optimal Estimation of Smooth Transport Maps with Kernel SoS. SIAM
Journal on Mathematics of Data Science, 6(2):311-342, 2024. [pdf]
B. Follain, F. Bach. Nonparametric Linear Feature
Learning in Regression Through Regularisation. Electronic
Journal of Statistics, 18(2):4075-4118. [pdf]
2023
A. Joudaki, H. Daneshmand, F. Bach. On the
impact of activation and normalization in obtaining isometric embeddings at
initialization. Advances in Neural Information Processing Systems (NeurIPS), 2023. [pdf]
L. Stewart, F. Bach, F. Llinares-López, Q. Berthet. Differentiable
Clustering with Perturbed Spanning Forests. Advances in Neural
Information Processing Systems (NeurIPS), 2023. [pdf] [code]
A. H. Ribeiro, D. Zachariah, F. Bach, T. B. Schön. Regularization properties
of adversarially-trained linear regression. Advances
in Neural Information Processing Systems (NeurIPS),
2023. [pdf]
S. Saremi, R. K. Srivastava, F. Bach. Universal
Smoothed Score Functions for Generative Modeling. Technical report, arXiv:2303.11669, 2023. [pdf]
B. Tzen, A. Raj, M. Raginsky,
F. Bach. Variational principles for mirror descent and mirror Langevin dynamics.
IEEE Control Systems Letters, 7:1542-1547, 2023. [pdf]
D. Holzmüller, F. Bach. Convergence rates for
non-log-concave sampling and log-partition estimation. Technical report,
arXiv:2303.0323, 2023. [pdf]
L. Pillaud-Vivien, F. Bach. Kernelized diffusion
maps. Proceedings of the Conference on Learning Theory (COLT),
2023. [pdf]
B. Woodworth, K. Mishchenko, F. Bach. Two losses are better than one: Faster
optimization using a cheaper proxy. Proceedings of the International
Conference on Machine Learning (ICML), 2023. [pdf]
F. Bach. On the relationship between multivariate splines and
infinitely-wide neural networks. Technical report, arXiv:2302.03459,
2023. [pdf]
F. Bach. Information theory with kernel methods. IEEE
Transactions in Information Theory, 69(2):752-775, 2023. [pdf]
F. Bach, A. Rudi. Exponential convergence of sum-of-squares hierarchies
for trigonometric polynomials. SIAM Journal on Optimization,
33(3): 2137-2159. [pdf]
L. Stewart, F. Bach, Q. Berthet, J.-P. Vert. Regression as
classification: Influence of task formulation on neural network features. Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS), 2023. [pdf]
A. Orvieto, A. Raj, H. Kersting, F.
Bach. Explicit regularization in overparametrized models via noise injection.
Proceedings of the International Conference on Artificial Intelligence and
Statistics (AISTATS), 2023. [pdf]
C. Moucer, A. Taylor, F. Bach. A systematic approach
to Lyapunov analyses of continuous-time models in convex optimization. SIAM
Journal on Optimization, 33(3):1558-1586, 2023. [pdf]
M. Lambert, S. Bonnabel, F. Bach. The limited-memory
recursive variational Gaussian approximation (L-RVGA). Statistics and
Computing, 33, 2023. [pdf]
2022
A. Défossez, L. Bottou, F. Bach,
N. Usunier. A Simple convergence proof of Adam
and Adagrad. Transactions on Machine Learning
Research, 2022. [pdf]
A. Lucchi, F. Proske, A. Orvieto, F. Bach, H. Kersting. On the
Theoretical Properties of Noise Correlation in Stochastic Optimization. Advances
in Neural Information Processing Systems (NeurIPS),
2022. [pdf]
K. Mishchenko, F. Bach, M. Even, B. Woodworth. Asynchronous SGD Beats
Minibatch SGD Under Arbitrary Delays. Advances in Neural Information
Processing Systems (NeurIPS), 2022. [pdf]
M. Lambert, S. Chewi, F. Bach, S. Bonnabel,
P. Rigollet. Variational inference via Wasserstein
gradient flows. Advances in Neural Information Processing Systems (NeurIPS), 2022. [pdf]
B. Dubois-Taine, F. Bach, Q. Berthet, A. Taylor. Fast Stochastic
Composite Minimization and an Accelerated Frank-Wolfe Algorithm under
Parallelization. Advances in Neural Information Processing Systems (NeurIPS), 2022. [pdf]
V. Cabannes, F. Bach, V. Perchet,
A. Rudi. Active Labeling: Streaming Stochastic Gradients. Advances
in Neural Information Processing Systems (NeurIPS),
2022. [pdf]
E. Berthier, Z. Kobeissi, F. Bach. A Non-asymptotic Analysis of
Non-parametric Temporal-Difference Learning. Advances in Neural
Information Processing Systems (NeurIPS), 2022. [pdf]
A. Joudaki, H. Daneshmand, F. Bach. Entropy
Maximization with Depth: A Variational Principle for Random Neural Networks.
Technical report, arXiv:2205.13076, 2022. [pdf]
H. Daneshmand, F. Bach. Polynomial-time sparse measure recovery.
Technical report, arXiv:2204.07879, 2022. [pdf]
Z. Kobeissi, F. Bach. On a Variance Reduction Correction of the
Temporal Difference for Policy Evaluation in the Stochastic Continuous Setting.
Technical report, arXiv:2202.07960, 2022. [pdf]
A. Orvieto, H. Kersting, F. Proske, F. Bach, A. Lucchi. Anticorrelated
Noise Injection for Improved Generalization. Proceedings of the
International Conference on Machine Learning (ICML), 2022. [pdf]
T. Ryffel, F. Bach, D. Pointcheval. Differential
Privacy Guarantees for Stochastic Gradient Langevin Dynamics. Technical report,
arXiv:2201.11980, 2022. [pdf]
M. Lambert, S. Bonnabel, F. Bach. The continuous-discrete
variational Kalman filter (CD-VKF). IEEE Conference on Decision and
Control, 2022. [pdf]
E. Berthier, J. Carpentier, A. Rudi, F. Bach. Infinite-Dimensional Sums-of-Squares
for Optimal Control. IEEE Conference on Decision and Control, 2022.
[pdf]
B. Woodworth, F. Bach, A. Rudi. Non-Convex
Optimization with Certificates and Fast Rates Through Kernel Sums of Squares. Proceedings
of the Conference on Learning Theory (COLT), 2022. [pdf]
A. Raj, F. Bach. Convergence of uncertainty sampling for active learning. Proceedings
of the International Conference on Machine Learning (ICML), 2022. [pdf]
F. Bach, L. Chizat. Gradient Descent on Infinitely
Wide Neural Networks: Global Convergence and Generalization. Proceedings
of the International Congress of Mathematicians, 2022. [pdf]
Y. Sun, F. Bach. Screening for a Reweighted Penalized Conditional
Gradient Method. Open Journal of Mathematical Optimization,
3:1-35, 2022 [pdf]
M. Barré, A. Taylor, F. Bach. A note on approximate accelerated forward-backward
methods with absolute and relative errors, and possibly strongly convex
objectives. Open Journal of Mathematical Optimization, 3(1),
2022. [pdf]
U. Marteau-Ferey, A. Rudi, F. Bach. Sampling from Arbitrary Functions
via PSD Models. Proceedings of the International Conference on
Artificial Intelligence and Statistics (AISTATS), 2022. [pdf]
A. Nowak-Vila, A. Rudi, F. Bach. On the Consistency of Max-Margin
Losses. Proceedings of the International Conference on Artificial
Intelligence and Statistics (AISTATS), 2022. [pdf]
M. Lambert, S. Bonnabel, F. Bach. The recursive
variational Gaussian approximation (R-VGA). Statistics and Computing,
32(1), 2022. [pdf]
2021
B. Muzellec, F. Bach, A. Rudi. Learning PSD-valued functions using
kernel sums-of-squares. Technical report, arXiv:2111.11306, 2021. [pdf]
B. Muzellec, F. Bach, A. Rudi. A
Note on Optimizing Distributions using Kernel Mean Embeddings. Technical report, arXiv:2106.09994, 2021. [pdf]
V. Cabannes, L. Pillaud-Vivien, F. Bach, A. Rudi. Overcoming
the curse of dimensionality with Laplacian regularization in semi-supervised learning. Advances in Neural Information
Processing Systems (NeurIPS), 2021. [pdf]
H. Daneshmand, A. Joudaki,
F. Bach. Batch Normalization Orthogonalizes Representations in
Deep Random Networks. Advances
in Neural Information Processing Systems (NeurIPS), 2021. [pdf]
M. Even, R. Berthier, F. Bach, N. Flammarion,
P. Gaillard, H. Hendrikx, L. Massoulié, A.
Taylor. A Continuized View on Nesterov
Acceleration for Stochastic Gradient Descent and Randomized Gossip. Advances in Neural Information
Processing Systems (NeurIPS), 2021. [pdf]
F. Bach. On the Effectiveness
of Richardson Extrapolation in Data Science. SIAM
Journal on Mathematics of Data Science,
3(4):1251-1277, 2021. [pdf] [slides]
A. Vacher, B. Muzellec, A. Rudi, F. Bach,
F.-X. Vialard. A Dimension-free
Computational Upper-bound for Smooth Optimal Transport Estimation. Proceedings of the Conference on
Learning Theory (COLT), 2021. [pdf]
V. Cabannes, F. Bach,
A. Rudi. Fast rates in structured prediction. Proceedings of the Conference on Learning
Theory (COLT), 2021. [pdf]
V. Cabannes, F. Bach,
A. Rudi. Disambiguation of weak supervision with exponential
convergence rates. Proceedings
of the International Conference on Machine Learning (ICML), 2021. [pdf]
A. Bietti, F. Bach. Deep
Equals Shallow for ReLU Networks in Kernel
Regimes. Proceedings of
the International Conference on Learning Representations (ICLR), 2021. [pdf]
A. Raj, F. Bach. Explicit Regularization
of Stochastic Gradient Methods through Duality. Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS), 2021. [pdf]
D. Ostrovskii, F.
Bach. Finite-sample Analysis of M-estimators using Self-concordance. Electronic
Journal of Statistics, 15(1):326-391, 2021. [pdf]
E. Berthier, J. Carpentier, F. Bach. Fast and Robust Stability Region
Estimation for Nonlinear Dynamical Systems. Proceedings of the
European Control Conference (ECC), 2021. [pdf]
R. M. Gower, P. Richtárik, F. Bach. Stochastic
Quasi-Gradient Methods: Variance Reduction via Jacobian Sketching. Mathematical
Programming, 188:135–192, 2021. [pdf]
2020
U. Marteau-Ferey, F. Bach, A. Rudi. Non-parametric Models for
Non-negative Functions. Advances in Neural Information Processing
Systems (NeurIPS), 2020. [pdf]
H. Hendrikx, F. Bach, L. Massoulié. Dual-Free
Stochastic Decentralized Optimization with Variance Reduction. Advances
in Neural Information Processing Systems (NeurIPS),
2020. [pdf]
R. Berthier, F. Bach, P. Gaillard. Tight Nonparametric Convergence
Rates for Stochastic Gradient Descent under the Noiseless Linear Model. Advances
in Neural Information Processing Systems (NeurIPS),
2020. [pdf]
Q. Berthet, M. Blondel, O. Teboul, M. Cuturi, J.-P.
Vert, F. Bach. Learning with Differentiable Perturbed Optimizers. Advances
in Neural Information Processing Systems (NeurIPS),
2020. [pdf] [video]
H. Daneshmand, J. Kohler, F. Bach, T. Hofmann, A. Lucchi. Batch
Normalization Provably Avoids Rank Collapse for Randomly Initialised
Deep Networks. Advances in Neural Information Processing Systems (NeurIPS), 2020. [pdf]
M. Barré, A. Taylor, F. Bach. Principled Analyses and Design of
First-Order Methods with Inexact Proximal Operators. Technical report,
arXiv:2006.06041, 2020. [pdf]
T. Eboli, A. Nowak-Vila, J. Sun, F. Bach, J. Ponce, A. Rudi. Structured
and Localized Image Restoration. Technical report, arXiv:2006.09261, 2020.
[pdf]
T. Ryffel, D. Pointcheval, F.
Bach. ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function
Secret Sharing. Technical report, arXiv:2006.04593, 2020. [pdf]
H. Hendrikx, F. Bach, L. Massoulié. An Optimal
Algorithm for Decentralized Finite Sum Optimization. Technical report,
arXiv:2005.10675, 2020. [pdf]
R. Sankaran, F. Bach, C. Bhattacharyya. Learning With Subquadratic Regularization : A Primal-Dual
Approach. Proceedings of the International Joint Conference on
Artificial Intelligence (IJCAI), 2020. [pdf]
A. Nowak-Vila, F. Bach, A. Rudi. Consistent
Structured Prediction with Max-Min Margin Markov Networks. Proceedings
of the International Conference on Machine Learning (ICML), 2020. [pdf]
V. Cabannes, A. Rudi, F. Bach. Structured Prediction
with Partial Labelling through the Infimum Loss. Proceedings of the
International Conference on Machine Learning (ICML), 2020. [pdf]
H. Hendrikx, L. Xiao, S. Bubeck, F. Bach, L. Massoulié. Statistically
Preconditioned Accelerated Gradient Method for Distributed Optimization. Proceedings
of the International Conference on Machine Learning (ICML), 2020. [pdf] [video]
M. Ballu, Q. Berthet, F. Bach. Stochastic
Optimization for Regularized Wasserstein Estimators. Proceedings of
the International Conference on Machine Learning (ICML), 2020. [pdf]
Y. Sun, F. Bach. Safe Screening for the Generalized Conditional
Gradient Method. Technical report, arXiv:2002.09718, 2020. [pdf]
L. Chizat, F. Bach. Implicit Bias of Gradient
Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss. Proceedings
of the Conference on Learning Theory (COLT) [pdf] [video] [slides]
E. Berthier, F. Bach. Max-Plus Linear Approximations for Deterministic
Continuous-State Markov Decision Processes. IEEE Control
Systems Letters, 4(3):767-772, 2020. [pdf]
L. Pillaud-Vivien, F. Bach, T. Lelièvre,
A. Rudi, G. Stoltz. Statistical Estimation of the Poincaré
constant and Application to Sampling Multimodal Distributions. Proceedings
of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
[pdf]
R. Berthier, F. Bach, P. Gaillard. Accelerated Gossip in Networks of
Given Dimension using Jacobi Polynomial Iterations. SIAM Journal on
Mathematics of Data Science 2(1):24-47, 2020. [pdf]
D. Scieur, A. d'Aspremont, F.
Bach. Regularized Nonlinear Acceleration. Mathematical
Programming, 179:47-83, 2020. [pdf]
R. M. Gower, M. Schmidt, F. Bach. P. Richtárik. Variance-Reduced
Methods for Machine Learning. Proceedings of the IEEE,
108(11):1968-1983, 2020. [pdf]
A. Dieuleveut, A. Durmus, F. Bach. Bridging
the Gap between Constant Step Size Stochastic Gradient Descent and Markov
Chains. Annals of Statistics, 48(3):1348-1382,
2020. [pdf]
2019
P. Askenazy, F. Bach. IA et emploi : Une menace
artificielle. Pouvoirs, 170, 33-41, 2019.
[pdf]
K. Scaman, F. Bach, S. Bubeck, Y.-T. Lee,
L. Massoulié. Optimal Convergence Rates for
Convex Distributed Optimization in Networks. Journal of Machine
Learning Research, 20(159):1-31, 2019. [pdf]
U. Marteau-Ferey, F. Bach, A. Rudi. Globally convergent Newton methods
for ill-conditioned generalized self-concordant Losses. Advances in
Neural Information Processing Systems (NeurIPS),
2019. [pdf] [supplement] [slides] [poster]
H. Hendrikx, F. Bach, L. Massoulié. An accelerated
decentralized stochastic proximal algorithm for finite Sums. Advances
in Neural Information Processing Systems (NeurIPS),
2019. [pdf] [supplement]
L. Chizat, E. Oyallon, F. Bach. On
Lazy Training in Differentiable Programming. Advances in Neural
Information Processing Systems (NeurIPS), 2019. [pdf] [supplement] [poster]
C. Ciliberto, F. Bach, A. Rudi. Localized
Structured Prediction. Advances in Neural Information Processing
Systems (NeurIPS), 2019. [pdf] [supplement]
J. Altschuler, F. Bach, A. Rudi, J. Niles-Weed. Massively scalable Sinkhorn
distances via the Nyström method. Advances in Neural Information Processing
Systems (NeurIPS), 2019. [pdf]
[supplement]
T. Ryffel, E. Dufour Sans, R. Gay, F. Bach, D. Pointcheval. Partially Encrypted Machine Learning using Functional Encryption. Advances in Neural Information Processing Systems
(NeurIPS), 2019. [pdf] [supplement]
K. S. Sesh Kumar, F. Bach, T. Pock. Fast Decomposable Submodular
Function Minimization using Constrained Total Variation. Advances in
Neural Information Processing Systems (NeurIPS), 2019.
[pdf] [supplement]
G. Gidel, F. Bach and S. Lacoste-Julien. Implicit Regularization of Discrete Gradient Dynamics
in Linear Neural Networks. Advances in Neural
Information Processing Systems (NeurIPS), 2019. [pdf] [supplement]
O. Sebbouh, N. Gazagnadou, S. Jelassi, F. Bach, R. Gower. Towards
closing the gap between the theory and practice of SVRG. Advances in Neural Information
Processing Systems (NeurIPS), 2019. [pdf] [supplement]
A. Kavis, K. Y. Levy, F. Bach, V. Cevher. UniXGrad:
A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained
Optimization. Advances in
Neural Information Processing Systems (NeurIPS), 2019. [pdf] [supplement]
F. Bach. Max-plus matching
pursuit for deterministic Markov decision processes. Technical report, arXiv-1906.08524, 2019. [pdf]
T. Shpakova, F. Bach,
M. E. Davies. Hyper-parameter Learning for Sparse Structured
Probabilistic Models. Proceedings
of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019. [pdf]
D. Babichev, D. Ostrovskii,
F. Bach. Efficient Primal-Dual Algorithms for Large-Scale Multiclass
Classification. Technical report, arXiv-1902.03755, 2019. [pdf]
U. Marteau-Ferey, D. Ostrovskii,
F. Bach, A. Rudi. Beyond Least-Squares: Fast Rates for Regularized
Empirical Risk Minimization through Self-Concordance. Proceedings of the
International Conference on Learning Theory (COLT), 2019. [pdf] [poster] [slides] [video]
F. Bach, K. Y. Levy. A Universal Algorithm for Variational Inequalities
Adaptive to Smoothness and Noise. Proceedings of the International
Conference on Learning Theory (COLT), 2019. [pdf]
A. Taylor, F. Bach. Stochastic first-order
methods: non-asymptotic and computer-aided analyses via potential functions. Proceedings
of the International Conference on Learning Theory (COLT), 2019. [pdf] [code] [slides] [video]
A. Nowak-Vila, F. Bach, A. Rudi. A
General Theory for Structured Prediction with Smooth Convex Surrogates. Technical
report, arXiv-1902.01958, 2019. [pdf]
H. V. Vo, F. Bach, M. Cho, K. Han, Y. Le Cun,
P. Perez, J. Ponce. Unsupervised Image Matching and Object Discovery as
Optimization. Proceedings of the Conference on Computer Vision and
Pattern Recognition (CVPR), 2019. [pdf]
H. Hendrikx,
L. Massoulié, F. Bach. Accelerated Decentralized Optimization with Local
Updates for Smooth and Strongly Convex Objectives. Proceedings of the International Conference on Artificial
Intelligence and Statistics (AISTATS), 2019. [pdf]
A. Nowak-Vila, F. Bach, A. Rudi. Sharp Analysis of Learning with
Discrete Losses. Proceedings of the International Conference on
Artificial Intelligence and Statistics (AISTATS), 2019. [pdf]
S. Vaswani, F. Bach, M. Schmidt. Fast and Faster Convergence of SGD for
Over-Parameterized Models and an Accelerated Perceptron. Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS), 2019. [pdf]
A. Genevay, L. Chizat, F. Bach,
M. Cuturi, G. Peyré. Sample
Complexity of Sinkhorn divergences. Proceedings of the
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
[pdf] [supplement]
P. Ablin, A. Gramfort, J.-F. Cardoso, F. Bach. Stochastic
algorithms with descent guarantees for ICA. Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS), 2019. [pdf]
A. Podosinnikova, A. Perry, A. Wein, F. Bach, A. d'Aspremont, D. Sontag. Overcomplete Independent Component
Analysis via SDP. Proceedings of the International Conference on
Artificial Intelligence and Statistics (AISTATS), 2019. [pdf]
F. Bach. Submodular Functions: from Discrete to Continuous Domains. Mathematical
Programming, 175(1), 419-459, 2019. [pdf] [code] [slides]
L. Rencker, F. Bach, W. Wang, M. D. Plumbley. Sparse Recovery and Dictionary Learning From Nonlinear Compressive Measurements. IEEE Transactions
in Signal Processing, 67(21):5659-5670, 2019. [pdf]
2018
L. Pillaud-Vivien, A.
Rudi, F. Bach. Statistical Optimality of Stochastic Gradient Descent
on Hard Learning Problems through Multiple Passes. Advances in Neural Information Processing Systems
(NIPS), 2018. [pdf] [supplement][slides]
L. Chizat, F. Bach. On
the Global Convergence of Gradient Descent for Over-parameterized Models using
Optimal Transport. Advances in Neural Information Processing
Systems (NeurIPS), 2018. [pdf] [supplement] [poster]
K. Scaman, F. Bach, S. Bubeck, Y.-T. Lee, L. Massoulié. Optimal
Algorithms for Non-Smooth Distributed Optimization in Networks. Advances
in Neural Information Processing Systems (NeurIPS), 2018.
[pdf] [supplement]
A. Defossez, N. Zeghidour, N. Usunier, L. Bottou, F. Bach. SING: Symbol-to-Instrument Neural Generator. Advances
in Neural Information Processing Systems (NeurIPS), 2018.
[pdf] [audio samples]
F. Bach. Efficient Algorithms for
Non-convex Isotonic Regression through Submodular Optimization. Advances
in Neural Information Processing Systems (NeurIPS), 2018.
[pdf] [supplement]
J. Tang, M. Golbabaee,
F. Bach, M. E. Davies. Rest-Katyusha: Exploiting the Solution's Structure
via Scheduled Restart Schemes. Advances in Neural Information
Processing Systems (NeurIPS), 2018. [pdf] [supplement]
E. Pauwels, F. Bach, J.-P. Vert. Relating Leverage Scores and Density
using Regularized Christoffel Functions. Advances in Neural
Information Processing Systems (NeurIPS), 2018.
[pdf] [supplement]
D. Scieur, E. Oyallon, A. d'Aspremont, F. Bach. Nonlinear Acceleration of Deep
Neural Networks. Technical report, arXiv-1805.09639, 2018. [pdf]
D. Babichev and F. Bach. Constant
Step Size Stochastic Gradient Descent for Probabilistic Modeling. Proceedings
of the conference on Uncertainty in Artificial Intelligence (UAI),
2018. [pdf]
T. Shpakova, F. Bach and A. Osokin. Marginal Weighted
Maximum Log-likelihood for Efficient Learning of Perturb-and-Map models. Proceedings
of the conference on Uncertainty in Artificial Intelligence (UAI),
2018. [pdf]
L. Rencker, F. Bach, W. Wang, M. D. Plumbley. Consistent dictionary learning for signal
declipping. International Conference on Latent
Variable Analysis and Signal Separation, 2018. [pdf]
L. Pillaud-Vivien, A. Rudi, F. Bach. Exponential
convergence of testing error for stochastic gradient methods. Proceedings
of the International Conference on Learning Theory (COLT), 2018. [pdf]
N. Tripuraneni, N. Flammarion, F. Bach, M. I. Jordan. Averaging
Stochastic Gradient Descent on Riemannian Manifolds. Proceedings of
the International Conference on Learning Theory (COLT), 2018. [pdf]
D. Babichev and F.
Bach. Slice inverse regression with score
functions. Electronic Journal of Statistics, 12(1):1507-1543, 2018. [pdf]
R. M. Gower, N. Le Roux, F. Bach. Tracking the gradients using the
Hessian: A new look at variance reducing stochastic methods. Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS), 2018. [pdf] [code]
A. Kundu, F. Bach, C. Bhattacharyya. Convex optimization over
intersection of simple sets: improved convergence rate guarantees via an exact
penalty approach. Proceedings of the International Conference on
Artificial Intelligence and Statistics (AISTATS), 2018. [pdf]
S. Reddi, M. Zaheer, S. Sra, B. Poczos,
F. Bach, R. Salakhutdinov, A. Smola. A
Generic Approach for Escaping Saddle points. Proceedings of the
International Conference on Artificial Intelligence and Statistics (AISTATS),
2018. [pdf]
M. El Halabi, F. Bach, V. Cevher. Combinatorial
Penalties: Which structures are preserved by convex relaxations? Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS), 2018. [pdf]
C. Dupuy and F. Bach. Learning
Determinantal Point Processes in Sublinear Time. Proceedings of the
International Conference on Artificial Intelligence and Statistics (AISTATS),
2018. [pdf]
T. Schatz, F. Bach, E. Dupoux. Evaluating automatic speech recognition
systems as quantitative models of cross-lingual phonetic category perception. Journal
of the Acoustical Society of America, Express Letters, 3(55), 2018. [pdf]
2017
A. Defossez, F. Bach. AdaBatch: Efficient Gradient Aggregation Rules
for Sequential and Parallel Stochastic Gradient Methods. Technical Report, Arxiv-1711.01761, 2017. [pdf]
J. Weed, F. Bach. Sharp asymptotic and finite-sample rates of convergence
of empirical measures in Wasserstein distance. Technical Report,
Arxiv-1707.00087, 2017. To appear in Bernoulli. [pdf]
D. Scieur, A. d'Aspremont, F. Bach. Nonlinear Acceleration of Stochastic
Algorithms. Advances in Neural Information Processing Systems
(NIPS), 2017. [pdf]
A. Osokin, F. Bach, S. Lacoste-Julien. On Structured Prediction
Theory with Calibrated Convex Surrogate Losses. Advances in Neural
Information Processing Systems (NIPS), 2017. [pdf]
D. Scieur, V. Roulet, F. Bach, A. d'Aspremont. Integration
Methods and Accelerated Optimization Algorithms. Advances in Neural
Information Processing Systems (NIPS), 2017. [pdf]
A. Dieuleveut, N.
Flammarion, and F. Bach. Harder, Better, Faster, Stronger Convergence
Rates for Least-Squares Regression. Journal of Machine Learning
Research, 18(101):1?51, 2017. [pdf]
N. Flammarion, P. Balamurugan, F.
Bach. Robust Discriminative Clustering with Sparse Regularizers. Journal
of Machine Learning Research, 18(80):1-50, 2017. [pdf]
F. Pedregosa, F. Bach, A. Gramfort. On the
Consistency of Ordinal Regression Methods. Journal of Machine Learning
Research, 18(55):1-35, 2017. [pdf]
F. Bach. On the Equivalence between Kernel Quadrature Rules and Random
Feature Expansions. Journal of Machine Learning Research,
18(19):1-38, 2017. [pdf]
F. Bach. Breaking the Curse of
Dimensionality with Convex Neural Networks. Journal of Machine
Learning Research, 18(19):1-53, 2017. [pdf]
K. Scaman, F. Bach, S. Bubeck, Y.-T. Lee,
L. Massoulié. Optimal algorithms for smooth
and strongly convex distributed optimization in networks. Proceedings
of the International Conference on Machine Learning (ICML), 2017. [pdf]
N. Flammarion, F.
Bach. Stochastic Composite Least-Squares Regression with convergence
rate O(1/n). Proceedings of the International Conference on
Learning Theory (COLT), 2017. [pdf]
R. Rezende, J. Zepeda, J.
Ponce, F. Bach, P. Pérez. Kernel square-loss exemplar machines for image
retrieval. Proceedings of the
Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [pdf] [code]
R. Sankaran, F. Bach, C. Bhattacharyya. Identifying groups of strongly
correlated variables through Smoothed Ordered Weighted L1-norms. Proceedings of the International Conference on Artificial
Intelligence and Statistics (AISTATS), 2017. [pdf]
C. Dupuy and F. Bach. Online but Accurate Inference for Latent Variable
Models with Local Gibbs Sampling. Journal of Machine Learning
Research, 18(126):1?45, 2017. [pdf] [code]
K. S. Sesh Kumar, F. Bach. Active-set
Methods for Submodular Minimization Problems. Journal of Machine
Learning Research, 18(132):1?31, 2017. [pdf]
F. Yanez, F. Bach. Primal-Dual
Algorithms for Non-negative Matrix Factorization with the Kullback-Leibler
Divergence. Proceedings of the International Conference on Acoustics,
Speech, and Signal Processing (ICASSP), 2017. [pdf] [code]
T. Schatz, R. Turnbull, F. Bach, E. Dupoux. A Quantitative Measure of
the Impact of Coarticulation on Phone Discriminability. Proceedings
of INTERSPEECH, 2017. [pdf]
2016
G. Obozinski and F. Bach. A unified perspective
on convex structured sparsity: Hierarchical, symmetric, submodular norms and
beyond. Technical report, HAL-01412385, 2016. [pdf]
T. Shpakova and F.
Bach. Parameter Learning for Log-supermodular
Distributions. Advances in Neural Information Processing Systems (NIPS).
[pdf]
D. Scieur, A. d'Aspremont, F. Bach. Regularized Nonlinear Acceleration. Advances
in Neural Information Processing Systems (NIPS). [pdf]
P. Balamurugan and F. Bach. Stochastic
Variance Reduction Methods for Saddle-Point Problems. Advances in
Neural Information Processing Systems (NIPS). [pdf] [code]
A. Genevay, M. Cuturi, G. Peyré, F. Bach. Stochastic Optimization for
Large-scale Optimal Transport. Advances in Neural Information
Processing Systems (NIPS). [pdf]
P. Germain, F. Bach, S.
Lacoste-Julien. PAC-Bayesian Theory Meets Bayesian Inference. Advances
in Neural Information Processing Systems (NIPS), 2016. [pdf]
M. Schmidt, N. Le Roux, F. Bach. Minimizing
Finite Sums with the Stochastic Average Gradient. Mathematical
Programming, 162(1):83-112, 2016. [pdf] [code]
A. Dieuleveut, F.
Bach. Non-parametric Stochastic Approximation with Large Step sizes. The
Annals of Statistics, 44(4):1363-1399, 2016. [pdf]
F. Bach, V. Perchet. Highly-Smooth Zero-th Order Online Optimization. Proceedings of the
Conference on Learning Theory (COLT), 2016. [pdf]
A. Podosinnikova, F. Bach, S. Lacoste-Julien. Beyond
CCA: Moment Matching for Multi-View Models. Proceedings of the International
Conference on Machine Learning (ICML), 2016. [pdf]
R. Lajugie, P. Bojanowski, P. Cuvillier, S. Arlot, F. Bach. A weakly-supervised discriminative
model for audio-to-score alignment. Proceedings of the
International Conference on Acoustics, Speech, and Signal Processing
(ICASSP), 2016. [pdf]
2015
A. Podosinnikova, F. Bach, S. Lacoste-Julien. Rethinking
LDA: moment matching for discrete ICA. Advances in Neural Information
Processing Systems (NIPS), 2015. [pdf]
R. Shivanna, B. Chatterjee, R. Sankaran, C. Bhattacharyya,
F. Bach. Spectral Norm Regularization of Orthonormal Representations
for Graph Transduction. Advances in Neural Information Processing
Systems (NIPS), 2015. [pdf]
P. Bojanowski, R. Lajugie,
E. Grave, F. Bach, I. Laptev, J. Ponce and C. Schmid. Weakly-Supervised
Alignment of Video With Text. Proceedings of
the International Conference on Computer Vision (ICCV), 2015. [pdf]
V. Roulet, F. Fogel, A. d'Aspremont, F. Bach. Supervised
Clustering in the Data Cube. Technical Report, ArXiv
1506.04908, 2015. [pdf]
F. Fogel, R. Jenatton, F. Bach,
A. d'Aspremont. Convex Relaxations for
Permutation Problems. SIAM Journal on Matrix Analysis and Application,
36(4):1465-1488, 2015. [pdf]
N. Flammarion, F. Bach. From Averaging to Acceleration, There is Only a Step-size. Proceedings of the International
Conference on Learning Theory (COLT), 2015. [pdf]
R. Lajugie, P. Bojanowski, S. Arlot
and F. Bach. Semidefinite and Spectral Relaxations for Multi-Label Classification.
Technical report, HAL- 01159321, 2015. [pdf]
K. S. Sesh Kumar, A. Barbero, S. Jegelka, S. Sra, F. Bach. Convex Optimization for Parallel
Energy Minimization. Technical report, HAL-01123492, 2015. [pdf]
N. Shervashidze and F. Bach. Learning the Structure for Structured
Sparsity. IEEE Transactions on Signal Processing,
63(18):4894-4902. [pdf] [code]
A. Defossez, F. Bach. Averaged Least-Mean-Square:
Bias-Variance Trade-offs and Optimal Sampling Distributions. Proceedings
of the International Conference on Artificial Intelligence and Statistics (AISTATS),
2015. [pdf]
S. Lacoste-Julien, F. Lindsten, F. Bach. Sequential Kernel Herding:
Frank-Wolfe Optimization for Particle Filtering. Proceedings of the
International Conference on Artificial Intelligence and Statistics (AISTATS),
2015. [pdf]
A. Bietti, F. Bach, A. Cont. An online
EM algorithm in hidden (semi-)Markov models for audio segmentation and clustering. Proceedings
of the International Conference on Acoustics, Speech, and Signal Processing
(ICASSP), 2015. [pdf]
F. Bach. Duality between subgradient and conditional
gradient methods. SIAM Journal of Optimization, 25(1):115-129,
2015. [pdf]
R. Gribonval, R. Jenatton, F.
Bach, M. Kleinsteuber, M. Seibert. Sample
Complexity of Dictionary Learning and Other Matrix Factorizations. IEEE
Transactions on Information Theory, 61(6):3469-3486, 2015. [pdf]
R. Gribonval, R. Jenatton, F. Bach. Sparse and spurious: dictionary
learning with noise and outliers. IEEE
Transactions on Information Theory, 61(11): 6298-6319, 2015. [pdf]
2014
J. Mairal, F. Bach, J. Ponce. Sparse Modeling for Image and Vision Processing. Foundations and Trends in Computer Vision, 8(2-3):85-283, 2014. [pdf]
D. Garreau, R. Lajugie,
S. Arlot and F. Bach. Metric Learning
for Temporal Sequence Alignment. Advances
in Neural Information Processing Systems (NIPS), 2014. [pdf]
A. Defazio, F. Bach, S. Lacoste-Julien. SAGA:
A Fast Incremental Gradient Method With Support for
Non-Strongly Convex Composite Objectives. Advances
in Neural Information Processing Systems (NIPS), 2014. [pdf]
P. Bojanowski, R. Lajugie,
F. Bach, I. Laptev, J. Ponce, C. Schmid and J. Sivic. Weakly-Supervised
Action Labeling in Videos Under Ordering Constraints. Proceedings of the European Conference on Computer
Vision (ECCV), 2014. [pdf]
E. Grave, G. Obozinski,
F. Bach. A Markovian approach to distributional semantics with application
to semantic compositionality. Proceedings
of the International Conference on Computational Linguistics (COLING),
2014. [pdf]
R. Lajugie,
S. Arlot and F. Bach. Large-Margin Metric Learning for Partitioning Problems. Proceedings of the International Conference on Machine Learning (ICML), 2014. [pdf]
F. Bach. Adaptivity of averaged stochastic
gradient descent to local strong convexity for logistic regression. Journal
of Machine Learning Research, 15(Feb):595-627, 2014. [pdf]
A. d'Aspremont, F. Bach, L. El Ghaoui. Approximation
Bounds for Sparse Principal Component Analysis. Mathematical Programming,
2014. [pdf]
T. Schatz, V. Peddinti, X.-N. Cao, F. Bach, H. Hynek,
E. Dupoux. Evaluating Speech Features with the Minimal-Pair ABX task
(II): Resistance to Noise. Proceedings of INTERSPEECH, 2014. [pdf]
2013
F. Bach. Learning with Submodular Functions: A Convex Optimization Perspective. Foundations and Trends in Machine
Learning, 6(2-3):145-373, 2013. [FOT website] [pdf] [slides]
F. Bach and E. Moulines. Non-strongly-convex
smooth stochastic approximation with convergence rate O(1/n). Advances
in Neural Information Processing Systems (NIPS). [pdf] [slides] [IPAM slides]
S. Jegelka, F. Bach,
S. Sra. Reflection methods for user-friendly submodular optimization. Advances in Neural Information
Processing Systems (NIPS). [pdf]
B. Mishra, G. Meyer, F. Bach, R. Sepulchre. Low-rank optimization with trace norm
penalty. SIAM Journal on
Optimization, 23(4):2124-2149, 2013. [pdf]
F. Fogel, R. Jenatton,
F. Bach, A. d'Aspremont. Convex
Relaxations for Permutation Problems.
Technical report, arXiv:1306.4805, 2013. To appear in Advances
in Neural Information Processing Systems (NIPS). [pdf]
K. S. Sesh Kumar and F. Bach. Maximizing
submodular functions using probabilistic graphical models. Technical report, HAL 00860575, 2013. [pdf]
A. Nelakanti,
C. Archambeau, J. Mairal, F. Bach, G. Bouchard. Structured Penalties for Log-linear Language Models. Proceedings of the Conference on Empirical
Methods in Natural Language Processing (EMNLP), 2013. [pdf]
P. Bojanowski, F. Bach, I. Laptev, J. Ponce,
C. Schmid and J. Sivic. Finding Actors and Actions in Movies. Proceedings of the International
Conference on Computer Vision (ICCV),
2013. [pdf]
F. Bach. Convex relaxations of
structured matrix factorizations.
Technical report, HAL 00861118, 2013. [pdf]
T. Schatz, V. Peddinti,
F. Bach, A. Jansen, H. Hynek, E. Dupoux. Evaluating speech
features with the Minimal-Pair ABX task: Analysis of the classical MFC/PLP
pipeline. Proceedings of
INTERSPEECH, 2013. [pdf]
Z. Harchaoui, F. Bach,
O. Cappe and E. Moulines. Kernel-Based
Methods for Hypothesis Testing: A Unified View. IEEE
Signal processing Magazine, 30(4): 87-97, 2013.
[pdf]
E. Grave, G. Obozinski,
F. Bach. Hidden Markov tree models for semantic class induction. Proceedings of the Conference on Computational Natural Language Learning
(CoNLL), 2013. [pdf]
E. Richard, F. Bach, and J.-P. Vert. Intersecting
singularities for multi-structured estimation. Proceedings
of the International Conference on Machine Learning (ICML), 2013. [pdf]
G. Rigaill, T. D. Hocking,
F. Bach, and J.-P. Vert. Learning Sparse Penalties for Change-Point
Detection using Max Margin Interval Regression. Proceedings
of the International Conference on Machine Learning (ICML), 2013. [pdf]
K. S. Sesh Kumar and F. Bach. Convex relaxations for learning
bounded-treewidth decomposable graphs. Proceedings of the International Conference on Machine Learning (ICML), 2013. [pdf]
T. D. Hocking, G. Schleiermacher, I. Janoueix-Lerosey,
O. Delattre, F. Bach, J.-P. Vert. Learning smoothing models of copy number
profiles using breakpoint annotations. BMC Bioinformatics, 14:1-15,
2013. [pdf]
F. Bach. Sharp
analysis of low-rank kernel matrix approximations. Technical report, HAL
00723365. Proceedings of the International Conference on Learning Theory
(COLT), 2013. [pdf]
N.
Le Roux, F. Bach. Local component analysis. Proceedings of the
International Conference on Learning Representations (ICLR), 2013. [pdf]
2012
S. Lacoste-Julien, M. Schmidt, F.
Bach. A Simpler Approach to Obtaining an O(1/t) Convergence Rate for
the Projected Stochastic Subgradient Method.
Technical report arXiv:1212.2002v2, December 2012. [pdf]
R. Jenatton, R. Gribonval and
F. Bach. Local stability and robustness of sparse dictionary learning
in the presence of noise. Technical report, HAL 00737152, 2012. [pdf]
G. Obozinski and
F. Bach. Convex Relaxation for Combinatorial Penalties. Technical report,
HAL 00694765, 2012. [pdf]
N. Le Roux, M. Schmidt, F. Bach. A
Stochastic Gradient Method with an Exponential Convergence Rate for
Strongly-Convex Optimization with Finite Training Sets. Advances in
Neural Information Processing Systems (NIPS). Technical report, HAL
00674995, 2012. [pdf] [slides]
H. Kadri, A. Rakotomamonjy, F. Bach, P. Preux. Multiple
Operator-valued Kernel Learning. Advances in Neural Information Processing
Systems (NIPS). Technical report, HAL 00677012, 2012. [pdf]
R. Jenatton, A. Gramfort, V.
Michel, G. Obozinski, E. Eger, F. Bach, B. Thirion. Multi-scale
Mining of fMRI data with Hierarchical Structured Sparsity. SIAM
Journal on Imaging Sciences, 2012, 5(3):835-856, 2012. [pdf]
M. Solnon, S. Arlot, F. Bach. Multi-task
Regression using Minimal Penalties. Journal of Machine Learning
Research, 13(Sep):2773-2812, 2012. [pdf]
A. Joulin and F. Bach. A convex relaxation for
weakly supervised classifiers. Proceedings of the International Conference
on Machine Learning (ICML), 2012. [pdf]
F. Bach, S. Lacoste-Julien, G. Obozinski. On the
Equivalence between Herding and Conditional Gradient Algorithms. Proceedings
of the International Conference on Machine Learning (ICML), 2012. [pdf]
A. Joulin, F. Bach, J. Ponce. Multi-Class Cosegmentation. Proceedings of the Conference on
Computer Vision and Pattern Recognition (CVPR), 2012. [pdf]
F. Bach, R. Jenatton, J. Mairal, G. Obozinski. Structured sparsity through convex
optimization. Statistical Science, 27(4):450-468, 2012. [pdf] [slides]
F. Bach, R. Jenatton, J. Mairal, G. Obozinski. Optimization with sparsity-inducing penalties. Foundations
and Trends in Machine Learning, 4(1):1-106, 2012. [FOT website] [pdf] [slides]
J. Mairal, F. Bach, J. Ponce. Task-Driven Dictionary Learning. IEEE
Transactions on Pattern Analysis and Machine Intelligence, 34(4):791-804,
2012. [pdf]
2011
C. Archambeau, F. Bach. Multiple
Gaussian process models. Technical Report Arxiv 110.5238,
2011. [pdf]
F. Bach, E. Moulines. Non-Asymptotic Analysis
of Stochastic Approximation Algorithms for Machine Learning. Advances
in Neural Information Processing Systems (NIPS), 2011. [pdf] [long-version-pdf-HAL]
M. Schmidt, N. Le Roux, F. Bach. Convergence Rates of Inexact
Proximal-Gradient Methods for Convex Optimization. Advances in
Neural Information Processing Systems (NIPS), 2011. [pdf] [long-version-pdf-HAL]
E. Grave, G. Obozinski, F. Bach. Trace Lasso:
a trace norm regularization for correlated designs. Advances in Neural
Information Processing Systems (NIPS), 2011. [pdf] [long-version-pdf-HAL]
F. Bach. Shaping Level Sets with
Submodular Functions. Advances in Neural Information Processing
Systems (NIPS), 2011. [pdf] [long-version-pdf-HAL]
B. Mishra, G. Meyer, F. Bach, R. Sepulchre. Low-rank
optimization with trace norm penalty. Technical report, Arxiv
1112.2318, 2011. [pdf]
R. Jenatton, J.-Y. Audibert and F. Bach. Structured
Variable Selection with Sparsity-inducing Norms. Journal of Machine
Learning Research, 12, 2777-2824, 2011. [pdf] [code]
J. Mairal, R. Jenatton, G. Obozinski,
F. Bach. Convex and Network Flow Optimization for Structured Sparsity. Journal
of Machine Learning Research, 12, 2681-2720. [pdf]
R. Jenatton, J. Mairal, G. Obozinski,
F. Bach. Proximal Methods for Hierarchical Sparse Coding. Journal
of Machine Learning Research, 12, 2297-2334, 2011. [pdf]
F. Couzinie-Devy, J. Mairal, F. Bach and J.
Ponce. Dictionary Learning for Deblurring and Digital Zoom.
Technical report, HAL : inria-
00627402, 2011. [pdf]
Y-L. Boureau, N. Le Roux, F. Bach, J. Ponce, and Y. LeCun. Ask the locals:
multi-way local pooling for image recognition. Proceedings of the International Conference on Computer Vision
(ICCV), 2011. [pdf]
S. Arlot, F. Bach. Data-driven Calibration of
Linear Estimators with Minimal Penalties. Technical report, HAL 00414774-v2,
2011. [pdf]
A. Lefevre, F. Bach, C. Fevotte. Online algorithms for Nonnegative
Matrix Factorization with the Itakura-Saito divergence. Technical report,
HAL 00602050, 2011. IEEE Workshop on Applications of Signal Processing
to Audio and Acoustics (WASPAA), 2011. [pdf]
T. Hocking, A. Joulin, F. Bach and J.-P. Vert. Clusterpath: an Algorithm for Clustering using Convex
Fusion Penalties. Proceedings of the International Conference
on Machine Learning (ICML), 2011. [pdf]
L. Benoit, J. Mairal, F. Bach, J. Ponce, Sparse Image Representation
with Epitomes. Proceedings of the Conference on Computer Vision and
Pattern Recognition (CVPR), 2011. [pdf]
A. Lefevre, F. Bach, C. Fevotte, Itakura-Saito nonnegative matrix
factorization with group sparsity, Proceedings of the International
Conference on Acoustics, Speech, and Signal Processing (ICASSP),
2011. [pdf]
F. Bach, R. Jenatton, J. Mairal and G. Obozinski. Convex optimization with sparsity-inducing
norms. In S. Sra, S. Nowozin,
S. J. Wright., editors, Optimization for Machine Learning, MIT Press, 2011. [pdf]
2010
F. Bach. Convex Analysis and Optimization with Submodular Functions: a
Tutorial. Technical report, HAL 00527714, 2010. [pdf]
F. Bach. Structured Sparsity-Inducing Norms through Submodular
Functions. Advances in Neural Information Processing Systems (NIPS),
2010. [pdf] [long version, arxiv] [slides]
J. Mairal, R. Jenatton, G. Obozinski,
F. Bach. Network Flow Algorithms for Structured Sparsity. Advances
in Neural Information Processing Systems (NIPS), 2010. [pdf]
A. Joulin, F. Bach, J.Ponce. Efficient Optimization for Discriminative
Latent Class Models. Advances in Neural Information Processing
Systems (NIPS), 2010. [pdf]
F. Bach, S. D. Ahipasaoglu, A. d'Aspremont. Convex
Relaxations for Subset Selection. Technical report Arxiv
1006-3601. [pdf]
M. Hoffman, D. Blei, F. Bach. Online Learning for Latent Dirichlet
Allocation. Advances in Neural Information Processing Systems
(NIPS), 2010. [pdf]
F. Bach, S. D. Ahipasaoglu, A. d'Aspremont. Convex
Relaxations for Subset Selection. Technical report, ArXiv
1006.3601, 2010. [pdf]
R. Jenatton, J. Mairal, G. Obozinski,
F. Bach. Proximal Methods for Sparse Hierarchical Dictionary Learning. Proceedings
of the International Conference on Machine Learning (ICML), 2010.
[pdf] [slides]
M. Journee, F. Bach, P.-A. Absil and R. Sepulchre. Low-Rank
Optimization on the Cone of Positive Semidefinite Matrices. SIAM
Journal on Optimization, 20(5):2327-2351, 2010. [pdf] [code]
A. Joulin, F. Bach, J.Ponce. Discriminative
Clustering for Image Co-segmentation. Proceedings of the Conference
on Computer Vision and Pattern Recognition (CVPR), 2010. [pdf]
[slides]
[code]
Y-L. Boureau, F. Bach, Y. LeCun,
J. Ponce. Learning Mid-Level Features For Recognition. Proceedings
of the Conference on Computer Vision and Pattern Recognition (CVPR), 2010. [pdf]
R. Jenatton, G. Obozinski, F.
Bach. Structured Sparse Principal Component Analysis. Proceedings
of the International Conference on Artificial Intelligence and Statistics (AISTATS),
2010. [pdf] [code]
M. Zaslavskiy, F. Bach and J.-P. Vert. Many-to-Many
Graph Matching: a Continuous Relaxation Approach. Technical report HAL-00465916, 2010. Proceedings of the European Conference
on Machine Learning (ECML). [pdf]
F. Bach. Self-Concordant Analysis for Logistic Regression. Electronic
Journal of Statistics, 4, 384-414, 2010. [pdf]
J. Mairal, F. Bach, J. Ponce, G. Sapiro. Online
Learning for Matrix Factorization and Sparse Coding. Journal of
Machine Learning Research, 11, 10-60, 2010. [pdf] [code]
A. Cord, F. Bach, D. Jeulin. Texture classification
by statistical learning from morphological image processing: application
to metallic surfaces. Journal of Microscopy, 239(2), 159-166,
2010. [pdf]
2009
P. Liang, F. Bach, G. Bouchard, M. I. Jordan. Asymptotically Optimal
Regularization in Smooth Parametric Models. Advances in Neural
Information Processing Systems (NIPS), 2009. [pdf]
S. Arlot, F. Bach. Data-driven Calibration of
Linear Estimators with Minimal Penalties. Advances in Neural Information
Processing Systems (NIPS), 2009. [techreport HAL 00414774 - pdf]
F. Bach. High-Dimensional Non-Linear Variable Selection through
Hierarchical Kernel Learning. Technical report, HAL 00413473, 2009. [pdf] [code] [slides]
J. Mairal, F. Bach, J. Ponce, G. Sapiro and A. Zisserman. Non-Local
Sparse Models for Image Restoration. International Conference on
Computer Vision (ICCV), 2009. [pdf]
O. Duchenne, I. Laptev, J. Sivic, F. Bach and J. Ponce. Automatic
Annotation of Human Actions in Video. International Conference on
Computer Vision (ICCV), 2009. [pdf]
J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online dictionary learning
for sparse coding. International Conference on Machine Learning
(ICML), 2009. [pdf]
O. Duchenne, F. Bach, I. Kweon, and J. Ponce. A tensor-based
algorithm for high-order graph matching. IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), 2009. [pdf]
M. Zaslavskiy, F. Bach and J.-P. Vert, Global
alignment of protein-protein interaction networks by graph matching methods. Bioinformatics,
25(12):1259-1267, 2009. [pdf]
F. Bach, Model-consistent sparse estimation through the bootstrap, Technical report HAL-00354771, 2009. [pdf]
J. Abernethy, F. Bach, T. Evgeniou, and J.-P.
Vert, A New Approach to Collaborative Filtering: Operator Estimation
with Spectral Regularization. Journal of Machine Learning Research,
10:803-826, 2009 [pdf]
M. Zaslavskiy, F.
Bach and J.-P. Vert, A path following algorithm for the graph matching
problem. IEEE Transactions on Pattern Analysis and Machine Intelligence,
31(12), 2227-2242, 2009. [pdf]
K. Fukumizu, F. Bach, and M. I. Jordan. Kernel
dimension reduction in regression. Annals of Statistics, 37(4),
1871-1905, 2009. [pdf]
2008
F. Bach, J. Mairal, J. Ponce, Convex
Sparse Matrix Factorizations, Technical report HAL-00345747, 2008. [pdf]
F. Bach. Exploring Large Feature Spaces with Hierarchical Multiple
Kernel Learning. Advances in Neural Information Processing Systems
(NIPS), 2008. [pdf] [HAL tech-report] [matlab code]
J. Mairal, F. Bach, J. Ponce, G. Sapiro and A. Zisserman. Supervised
Dictionary Learning. Advances in Neural Information Processing
Systems (NIPS), 2008. [pdf]
L. Jacob, F. Bach, J.-P. Vert. Clustered Multi-Task Learning: A Convex
Formulation. Advances in Neural Information Processing Systems
(NIPS), 2008. [pdf]
Z. Harchaoui, F. Bach, and E. Moulines. Kernel change-point analysis, Advances
in Neural Information Processing Systems (NIPS), 2008. [pdf]
C. Archambeau, F. Bach. Sparse
probabilistic projections, Advances in Neural Information
Processing Systems (NIPS), 2008. [pdf]
A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. SimpleMKL. Journal
of Machine Learning Research, 9, 2491-2521, 2008. [pdf] [code]
J. Mairal, M. Leordeanu, F. Bach, M. Hebert and J.
Ponce. Discriminative Sparse Image Models for Class-Specific Edge
Detection and Image Interpretation. Proceedings of
the European Conference on Computer Vision (ECCV), 2008. [pdf]
F. Bach. Bolasso: model consistent
Lasso estimation through the bootstrap. Proceedings of the
Twenty-fifth International Conference on Machine Learning (ICML), 2008. [pdf] [slides]
A. d'Aspremont, F.
Bach and L. El Ghaoui. Optimal solutions
for sparse principal component analysis. Journal of Machine Learning
Research, 9, 1269-1294. [pdf] [source code] [slides]
F. Bach. Consistency of the group
Lasso and multiple kernel learning, Journal of Machine Learning Research, 9,
1179-1225, 2008. [pdf] [slides]
F. Bach. Consistency of trace norm minimization, Journal of
Machine Learning Research,
9, 1019-1048, 2008. [pdf]
J. Mairal, F. Bach, J. Ponce, G. Sapiro and A. Zisserman. Discriminative
Learned Dictionaries for Local Image Analysis, Proceedings of the
Conference on Computer Vision and Pattern Recognition (CVPR), 2008. [pdf]
F. Bach. Graph kernels between
point clouds. Proceedings of the Twenty-fifth International
Conference on Machine Learning (ICML), 2008. [pdf]
2007
Z. Harchaoui,
F. Bach, and E. Moulines. Testing for Homogeneity with Kernel Fisher Discriminant
Analysis, Advances in Neural Information Processing Systems
(NIPS) 20, 2007. [pdf] [long version, HAL-00270806, 2008]
F. Bach and Z. Harchaoui. DIFFRAC
: a discriminative and flexible framework for clustering, Advances
in Neural Information Processing Systems (NIPS) 20, 2007. [pdf] [slides]
A. M. Cord, D. Jeulin and F. Bach. Segmentation
of random textures by morphological and linear operators. Proceedings
of
the Eigth
International Symposium on Mathematical Morphology (ISMM), 2007. [pdf]
A. d'Aspremont, F. Bach
and L. El Ghaoui. Full regularization
path for sparse principal component analysis. Proceedings of the
Twenty-fourth International Conference on Machine Learning (ICML), 2007. [pdf] [tech-report, arXiv]
A. Rakotomamonjy, F.
Bach, S. Canu, and Y. Grandvalet. More
Efficiency in Multiple Kernel Learning, Proceedings of the Twenty-fourth International
Conference on Machine Learning (ICML), 2007.
[pdf]
Z. Harchaoui and F. Bach. Image
classification with segmentation graph kernels, Proceedings
of the Conference on Computer Vision and Pattern Recognition (CVPR), 2007. [pdf] [presentation]
J. Louradour, K. Daoudi
and F. Bach. Feature Space Mahalanobis
Sequence Kernels: Application to SVM Speaker Verification. IEEE Transactions on Audio,
Speech and Language Processing, 15
(8), 2465-2475, 2007.
Y. Yamanishi, F. Bach., and J.-P. Vert. Glycan
Classification with Tree Kernels, Bioinformatics, 23(10):1211-1216, 2007. [pdf] [web supplements]
K. Fukumizu, F. Bach,
A. Gretton. Consistency of Kernel Canonical Correlation Analysis. Journal of Machine Learning
Research, 8, 361-383, 2007. [pdf]
2006
J. Abernethy, F. Bach, T. Evgeniou, and J.-P. Vert. Low-rank matrix factorization
with attributes. Technical report
N24/06/MM, Ecole des Mines de Paris, 2006. [pdf] [ArXiv]
F. Bach, Active learning for misspecified generalized
linear models, Advances in Neural Information Processing Systems
(NIPS) 19, 2006. [pdf] [tech-report]
F. Bach, M. I. Jordan, Learning spectral clustering, with application
to speech separation, Journal of Machine Learning Research, 7,
1963-2001, 2006. [pdf] [speech samples]
F. Bach, D. Heckerman, E. Horvitz, Considering
cost asymmetry in learning classifiers, Journal of Machine Learning
Research, 7, 1713-1741, 2006. [pdf]
J. Louradour,
K. Daoudi, F. Bach, SVM Speaker
Verification using an Incomplete Cholesky Decomposition Sequence Kernel. Proc. Odyssey, San Juan, Porto Rico, 2006. [pdf] [slides]
2005
K. Fukumizu, F. Bach, Arthur Gretton. Consistency
of Kernel Canonical Correlation Analysis. Advances in Neural Information
Processing Systems (NIPS) 18, 2005. [pdf]
F. Bach, M. I. Jordan. Predictive
low-rank decomposition for kernel methods. Proceedings of the
Twenty-second International Conference on Machine Learning (ICML),
2005. [pdf] [matlab/C code] [slides]
F. Bach, M. I. Jordan. A probabilistic interpretation of canonical
correlation analysis. Technical Report 688, Department of Statistics,
University of California, Berkeley, 2005 [pdf]
F. Bach, D. Heckerman, E. Horvitz, On
the path to an ideal ROC Curve: considering cost asymmetry in learning
classifiers, Tenth International Workshop on Artificial
Intelligence and Statistics (AISTATS), 2005 [pdf] [pdf, technical report MSR-TR-2004-24] [slides]
F. Bach, M. I. Jordan. Discriminative training of hidden Markov models
for multiple pitch tracking, Proceedings of the
International Conference on Acoustics, Speech, and Signal Processing (ICASSP),
2005 [pdf] [pdf, in French]
2004
F. Bach, M. I. Jordan. Blind one-microphone
speech separation: A spectral learning approach. Advances in Neural
Information Processing Systems (NIPS) 17, 2004. [pdf] [speech samples] [slides]
F. Bach, R. Thibaux,
M. I. Jordan. Computing regularization paths for learning multiple
kernels.. Advances
in Neural Information Processing Systems (NIPS) 17, 2004. [pdf] [matlab code] [slides]
F. Bach, M. I. Jordan. Learning
graphical models for stationary time series, IEEE Transactions on
Signal Processing, vol. 52, no. 8, 2189-2199, 2004. [pdf]
F. Bach, G. R. G. Lanckriet,
M. I. Jordan. Multiple Kernel Learning, Conic Duality, and the SMO Algorithm. Proceedings
of the Twenty-first International Conference on Machine Learning, 2004 [pdf] [tech-report]
K. Fukumizu, F. Bach,
M. I. Jordan. Dimensionality reduction for supervised learning with
reproducing kernel Hilbert spaces, Journal of Machine Learning
Research, 5, 73-99, 2004. [pdf]
2003
F. Bach, M. I. Jordan. Beyond
independent components: trees and clusters, Journal of Machine
Learning Research, 4, 1205-1233, 2003. [pdf] [matlab code]
F. Bach, M. I. Jordan. Learning
spectral clustering, Advances in Neural Information Processing
Systems (NIPS) 16, 2004. [pdf] [tech-report]
Kenji Fukumizu, F.
Bach, and M. I. Jordan. Kernel dimensionality reduction for supervised
learning, Advances in Neural Information Processing Systems
(NIPS) 16, 2004. [pdf] [pdf,
in Japanese]
F. Bach, M. I. Jordan. Analyse en composantes independantes et reseaux Bayesiens, Dix-neuvième
colloque GRETSI sur le traitement du signal et des images, 2003. [ps] [pdf] [matlab code]
F. Bach, M. I. Jordan. Finding
clusters in independent component analysis, Fourth
International Symposium on Independent Component Analysis and Blind Signal
Separation, 2003. [pdf] [matlab code]
F. Bach, M. I. Jordan. Kernel independent component analysis, Proceedings
of the International Conference on Acoustics, Speech, and Signal Processing
(ICASSP), 2003. [pdf] [long version (pdf)] [matlab code]
2002
F. Bach, M. I. Jordan. Learning
graphical models with Mercer kernels, Advances in Neural
Information Processing Systems (NIPS) 15, 2003. [pdf]
F. Bach, M. I. Jordan. Kernel
independent component analysis, Journal of Machine Learning
Research, 3, 1-48, 2002. [pdf] [matlab code]
F. Bach, M. I. Jordan. Tree-dependent component analysis, Uncertainty
in Artificial Intelligence (UAI): Proceedings of the Eighteenth Conference,
2002. [pdf] [matlab code]
2001
F. Bach, M. I. Jordan. Thin
junction trees, Advances in Neural Information Processing Systems
(NIPS) 14, 2002. [pdf]
Software
Minimizing Finite Sums with the Stochastic
Average Gradient
Submodular optimization (matlab)
Discriminative clustering for image
co-segmentation (matlab/C)
Structured variable selection with
sparsity-inducing norms (matlab)
Structured sparse PCA (matlab)
Sparse modeling software - SPAM (C)
Hierarchical kernel learning - version 3.0 (matlab)
Diffrac - version 1.0 (matlab)
Grouplasso - version 1.0 (matlab)
SimpleMKL - version 1.0 (matlab)
Support Kernel Machine - Multiple kernel
learning (matlab)
Predictive low-rank decomposition for kernel
methods - version 1.0 (matlab/C)
Computing regularization paths for multiple kernel learning - version
1.0 (matlab)
Tree-dependent component analysis - version 1.0
(matlab)
Tutorials / mini-courses (older ones)
August 2018: Machine Learning Summer School, Madrid - Large-scale machine
learning and convex optimization [slides]
September 2017: StatMathAppli 2017, Fréjus - Large-scale machine learning and
convex optimization [slides]
May 2017: SIAM
Conference on Optimization mini-tutorial on "Stochastic Variance-Reduced
Optimization for Machine Learning" [part 1] [part 2]
December 2016: NIPS 2016 Tutorial on "Large-Scale Optimization: Beyond
Stochastic Gradient Descent and Convexity" [part 1] [part 2]
July 2016: IFCAM summer
school, Indian Institute of Science, Bangalore - Large-scale machine
learning and convex optimization [slides]
May 2016: Machine Learning Summer School, Cadiz - Large-scale machine learning
and convex optimization [slides]
February 2016: Statistical learning week, CIRM, Luminy -
Large-scale machine learning and convex optimization [slides]
January 2016: Winter School on Advances in
Mathematics of Signal Processing, Bonn - Large-scale machine learning and convex optimization [slides]
July 2014: IFCAM Summer School, Indian Institute of Science, Bangalore
- Large-scale machine learning and convex optimization [slides]
March 2014: YES Workshop, Eurandom,
Eindhoven - Large-scale machine learning and convex optimization [slides]
September 2013: Fourth Cargese Workshop on Combinatorial
Optimization - Machine learning and convex
optimization with submodular functions
September 2012: Machine Learning Summer School,
Kyoto - Learning with
submodular functions [slides] [notes]
July 2012: Computer Vision and Machine Learning
Summer School, Grenoble - Kernel methods and sparse methods
for computer vision
July 2012: International Computer Vision Summer School,
Sicily - Structured sparsity through convex
optimization
July 2011: Computer Vision and Machine Learning
Summer School, Paris - Kernel methods and sparse methods
for computer vision
September 2010: ECML/PKDD Tutorial on Sparse methods for machine
learning (Theory and algorithms)
July 2010: Computer Vision and Machine Learning
Summer School, Grenoble - Kernel methods and sparse methods
for computer vision
July 2010: Signal processing summer school, Peyresq - Sparse method for machine learning
June 2010: CVPR Tutorial on Sparse Coding and
Dictionary Learning for Image Analysis - slides of ML part
December 2009: NIPS Tutorial on Sparse methods for
machine learning (Theory and algorithms)
September 2009: ICCV Tutorial on Sparse Coding and Dictionary Learning
for Image Analysis
September 2008: Machine Learning Summer School - Ile de Re - Learning
with sparsity inducing norms (slides)
October 2008: ECCV Tutorial on Supervised Learning: Introduction - Part I (Theory) - Part II (Algorithms)
January 2008: Workshop RASMA, Franceville, Gabon -
Introduction to kernel methods (slides in French)
Courses (older ones)
Fall 2020: Learning theory from first
principles - Mastere M2 Mash
Spring 2020: Optimisation et Apprentissage
Statistique - Master M2 "Mathematiques de l'aleatoire" - Universite Paris-Sud (Orsay)
Spring 2020: Machine Learning - Masters ICFP, Ecole
Normale Superieure
Fall 2018: Statistical machine learning - Master M1 -
Ecole Normale Superieure (Paris)
Fall 2018: Statistical machine learning - Master M1 -
Ecole Normale Superieure (Paris)
Spring 2018: Optimisation et Apprentissage
Statistique - Master M2 "Mathematiques de l'aleatoire" - Universite Paris-Sud (Orsay)
Fall 2017: Statistical machine learning - Master M1 -
Ecole Normale Superieure (Paris)
Spring 2017: Optimisation et Apprentissage
Statistique - Master M2 "Mathematiques de l'aleatoire" - Universite Paris-Sud (Orsay)
Spring 2016: Optimisation et Apprentissage
Statistique - Master M2 "Mathematiques de l'aleatoire" - Universite Paris-Sud (Orsay)
Fall 2014: Statistical machine learning -
Master M1 - Ecole Normale Superieure (Paris)
Fall 2014: An introduction to graphical models
- Master M2 "Mathematiques, Vision, Apprentissage" - Ecole Normale Superieure
de Cachan
Spring 2014: Statistical machine learning -
Master M2 "Probabilites et Statistiques"
- Universite Paris-Sud (Orsay)
Spring 2013: Statistical machine learning - Master
M2 "Probabilites et Statistiques"
- Universite Paris-Sud (Orsay)
Spring 2013: Statistical machine learning - Filiere Math/Info - L3 - Ecole Normale Superieure
(Paris)
Spring 2012: Statistical machine learning - Filiere Math/Info - L3 - Ecole Normale Superieure
(Paris)
Spring 2012: Statistical machine learning -
Master M2 "Probabilites et Statistiques"
- Universite Paris-Sud (Orsay)
Fall 2011: An introduction to graphical models - Master M2
"Mathematiques, Vision, Apprentissage"
- Ecole Normale Superieure de Cachan
Spring 2011: Statistical machine learning - Master M2 "Probabilites et Statistiques"
- Universite Paris-Sud (Orsay)
Fall 2010: An introduction to graphical models - Master M2
"Mathematiques, Vision,Apprentissage"
- Ecole Normale Superieure de Cachan
Spring 2010: Statistical machine learning - Master
M2 "Probabilites et Statistiques"
- Universite Paris-Sud (Orsay)
Fall 2009: An introduction to graphical models - Master M2
"Mathematiques, Vision,Apprentissage"
- Ecole Normale Superieure de Cachan
Fall 2008: An introduction to graphical models -
Master M2 "Mathematiques, Vision, Apprentissage" - Ecole Normale Superieure
de Cachan
May 2008: Probabilistic modelling and graphical
models: Enseignement Specialise
- Ecole des Mines de Paris
Fall 2007: An introduction to graphical models - Master M2
"Mathematiques, Vision, Apprentissage"
- Ecole Normale Superieure de Cachan
May 2007: Probabilistic modelling and graphical models: Enseignement Specialise - Ecole des
Mines de Paris
Fall 2006: An introduction to graphical models - Master M2
"Mathematiques, Vision, Apprentissage"
- Ecole Normale Superieure de Cachan
Fall 2005: An introduction to graphical models - Master M2
"Mathematiques, Vision, Apprentissage"
- Ecole Normale Superieure de Cachan