|
|
Francis Bach INRIA - SIERRA
project-team PSL Research University CS61534 75647 Paris Cedex
|
Blog - Tutorials - Courses - Students - Alumni - Publications - Software
Book: Learning Theory from
First Principles, published
in December 2024 at MIT Press
Final pdf version
Code (python, Matlab)
Blog post
I am
a researcher at INRIA, leading since 2011 the SIERRA project-team, which is part of the Computer Science Department at Ecole Normale
Supérieure, and a joint team between CNRS,
ENS and INRIA. I completed my Ph.D. in Computer Science at U.C. Berkeley, working
with Professor Michael Jordan, and
spent two years in the Mathematical
Morphology group at Ecole des Mines de Paris, I then joined the WILLOW project-team at
INRIA/Ecole Normale 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)
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
David
Holzmüller
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 Follain
Pascal Germain, Assistant professor, Université Laval, Canada
Robert Gower, Research Scientist,
Flatiron Institute, New York
Edouard Grave, Research scientist, Kyutai, Paris
Zaid Harchaoui, Associate Professor, University of Washington
Hadrien Hendrikx, Researcher, Inria Grenoble
Toby Hocking, Assistant
Professor, Northern Arizona University
Nicolas
Flammarion, Assistant Professor, Ecole Polytechnique
Federale de Lausanne
Fajwel Fogel, Research scientist, Sancare
Rodolphe Jenatton, CTO, Bioptimus
Armand Joulin, Research scientist, Google Deepmind, Paris
Hans Kersting, Research Scientist, Yahoo Research
Ziad Kobeissi, Researcher at Inria, Saclay
Sesh Kumar, Research
fellow, Imperial College Business School
Simon Lacoste-Julien, Professor,
Université de Montréal
Remi Lajugie, Professeur d'Informatique, Lycée Janson de Sailly,
Paris
Marc Lambert
Augustin Lefèvre, Data scientist, YKems
Ivan Lerner
Nicolas Le Roux, Researcher, Microsoft Research, Montréal
Ronny Luss, Researcher, IBM Research
Julien Mairal, Researcher at Inria, Grenoble
Ulysse Marteau-Ferey, Research scientist, Owkin
Bamdev Mishra, Research scientist, Amazon Bangalore
Céline Moucer, Ministère du Budget
Boris Muzellec, Research scientist, Owkin
Anil Nelakanti, Research scientist, Amazon Bangalore
Alex Nowak-Vila, Research
scientist, Owkin
Guillaume Obozinski, Deputy Chief Data Scientist, Swiss Data Science Center
Dmitrii Ostrovskii, Postdoctoral researcher, University of South California
Loucas Pillaud-Vivien, Postdoctoral researcher, Ecole Polytechnique Federale de Lausanne
Anastasia Podosinnikova, Postdoctoral fellow, MIT
Fabian
Pedregosa, Researcher, Google Brain, Montréal
Rafael Rezende, Postdoctoral researcher, Naver Labs, Grenoble
Anant Raj, Assistant Professor, Indian Institute of Science, Bangalore, India
Théo Ryffel, Arkhn
Thomas Schatz, Assistant professor, Aix-Marseille Université,
Marseille
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
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