Francis Bach

Professor

Departement d'Informatique de l'Ecole Normale Superieure, PSL Research University

Show Academic Trajectory

Ego Network

D-Core

Research Interests

Author Statistics

Experience

Education

Bio

I am interested in statistical machine learning, and especially in graphical models, sparse methods, kernel-based learning, convex optimization vision and signal processing.

Papers377 papers

Sort

By YearBy Citation

Explicit Regularization of Stochastic Gradient Methods through Duality

Raj Anant,Bach Francis

AISTATS, pp.1882-1890, (2021)

EIBibtex

Deep Equals Shallow for ReLU Networks in Kernel Regimes

Alberto Bietti,Francis Bach

international conference on learning representations, (2021)

Bibtex

Variance-Reduced Methods for Machine Learning

Robert M. Gower,Mark Schmidt,Francis Bach,Peter Richtarik

PROCEEDINGS OF THE IEEE, no. 11 (2020): 1968-1983

Cited by2EIBibtex

BRIDGING THE GAP BETWEEN CONSTANT STEP SIZE STOCHASTIC GRADIENT DESCENT AND MARKOV CHAINS

Aymeric Dieuleveut,Alain Durmus,Francis Bach

ANNALS OF STATISTICS, no. 3 (2020): 1348-1382

Bibtex

Stochastic quasi-gradient methods: variance reduction via Jacobian sketching

Robert M. Gower,Peter Richtárik,Francis Bach

Mathematical Programming, (2020)

Bibtex

Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss

Chizat Lenaic,Bach Francis

COLT, pp.1305-1338, (2020)

Cited by27EIBibtex

Learning with Differentiable Pertubed Optimizers

Berthet Quentin,Blondel Mathieu,Teboul Olivier,Cuturi Marco,Vert Jean-Philippe,Bach Francis

NIPS 2020, (2020)

Cited by9EIBibtex

Stochastic Optimization for Regularized Wasserstein Estimators

Ballu Marin,Berthet Quentin,Bach Francis

ICML, pp.602-612, (2020)

Cited by2EIBibtex

Max-Plus Linear Approximations for Deterministic Continuous-State Markov Decision Processes

Eloise Berthier,Francis Bach

ieee control systems letters, pp.1-1, (2020)

Bibtex

Safe Screening for the Generalized Conditional Gradient Method

Sun Yifan,Bach Francis

Bibtex

Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization

Hendrikx Hadrien,Xiao Lin,Bubeck Sebastien,Bach Francis,Massoulie Laurent

ICML, pp.4203-4227, (2020)

Cited by9EIBibtex

Structured Prediction with Partial Labelling through the Infimum Loss

Cabannes Vivien,Rudi Alessandro,Bach Francis

ICML, pp.1230-1239, (2020)

Cited by1EIBibtex

Theoretical Understanding of Batch-normalization: A Markov Chain Perspective

Daneshmand Hadi, Kohler Jonas,Bach Francis,Hofmann Thomas,Lucchi Aurelien

Cited by3Bibtex

On the Convergence of Adam and Adagrad

Défossez Alexandre,Bottou Léon,Bach Francis,Usunier Nicolas

Cited by13Bibtex

Finding Global Minima via Kernel Approximations

Alessandro Rudi, Ulysse Marteau-Ferey,Francis Bach

Bibtex

Consistent Structured Prediction with Max-Min Margin Markov Networks

Alex Nowak,Francis Bach,Alessandro Rudi

ICML, pp.7381-7391, (2020)

EIBibtex

Structured Prediction with Partial Labelling through the Infimum Loss

Vivien Cabannnes,Francis Bach,Alessandro Rudi

international conference on machine learning, (2020)

Bibtex

ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing

Ryffel Théo, Pointcheval David,Bach Francis

Cited by2Bibtex

Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model

Berthier Raphaël,Bach Francis,Gaillard Pierre

NIPS 2020, (2020)

EIBibtex

Dual-Free Stochastic Decentralized Optimization with Variance Reduction

Hadrien Hendrikx,Francis Bach,Laurent Massoulié

NIPS 2020, (2020)

Cited by1EIBibtex

View All

Base Info Papers Patents Projects

Francis BachBinding

Papers377 papers

Francis Bach