← Authors

Francis R. Bach

46 papers · 2002–2023 · 2 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (2) πŸƒ Academic Marathon (21) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (15) πŸ—ΊοΈ Taxonomy Completionist (29) 🧭 Keyword Pioneer 🌈 Renaissance Researcher (7) 🐣 Hot Topic Early Bird 🐺 Lone Wolf (6) 🏠 Conference Loyalist (37) 🌟 Keyword Trendsetter Combo (9) πŸ† Keyword Champion (8) 🌱 Topic Pioneer πŸ”¬ Deep Specialist (10) πŸ—ƒοΈ Keyword Collector (127) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter ⚑ Prolific Year (6) πŸ’Ž Century Club (46) πŸ”₯ Unstoppable (7)

Conferences

NIPS (37) JMLR (9)

Top co-authors

Michael I. Jordan (5) ZaΓ―d Harchaoui (3) Alessandro Rudi (3) Quentin Berthet (3) Hadi Daneshmand (3) Vivien Cabannes (2) Jean Ponce (2) RaphaΓ«l Berthier (2) Mark Schmidt (2) Laurent MassouliΓ© (2)

Research topics

Applications (1) Statistics (1)

Keywords

convex optimization (10) kernel methods (9) stochastic gradient descent (8) reproducing kernel hilbert space (5) statistical learning (4) multiple kernel learning (4) semi-supervised learning (3) variational inference (3) stochastic gradient (3) feature selection (3) convergence rate (3) sparse optimization (3) active learning (2) multi-task learning (2) distributed learning (2) stochastic optimization (2) asymptotic analysis (2) independent component analysis (2) variable selection (2) model selection (2)

Papers

Regularization properties of adversarially-trained linear regression NIPS 2023
Differentiable Clustering with Perturbed Spanning Forests NIPS 2023
On the impact of activation and normalization in obtaining isometric embeddings at initialization NIPS 2023
Active Labeling: Streaming Stochastic Gradients NIPS 2022
Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization NIPS 2022
A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning NIPS 2022
On the Theoretical Properties of Noise Correlation in Stochastic Optimization NIPS 2022
Variational inference via Wasserstein gradient flows NIPS 2022
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays NIPS 2022
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning NIPS 2021
Batch Normalization Orthogonalizes Representations in Deep Random Networks NIPS 2021
Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms NIPS 2021
Batch normalization provably avoids ranks collapse for randomly initialised deep networks NIPS 2020
Dual-Free Stochastic Decentralized Optimization with Variance Reduction NIPS 2020
Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model NIPS 2020
Learning with Differentiable Pertubed Optimizers NIPS 2020
Non-parametric Models for Non-negative Functions NIPS 2020
Multiple Operator-valued Kernel Learning NIPS 2012
A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets NIPS 2012
Shaping Level Sets with Submodular Functions NIPS 2011
Trace Lasso: a trace norm regularization for correlated designs NIPS 2011
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning NIPS 2011
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization NIPS 2011
Efficient Optimization for Discriminative Latent Class Models NIPS 2010
Online Learning for Latent Dirichlet Allocation NIPS 2010
Structured sparsity-inducing norms through submodular functions NIPS 2010
Network Flow Algorithms for Structured Sparsity NIPS 2010
Asymptotically Optimal Regularization in Smooth Parametric Models NIPS 2009
Data-driven calibration of linear estimators with minimal penalties NIPS 2009
SimpleMKL JMLR 2008
Kernel Change-point Analysis NIPS 2008
Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning NIPS 2008
Supervised Dictionary Learning NIPS 2008
Sparse probabilistic projections NIPS 2008
Clustered Multi-Task Learning: A Convex Formulation NIPS 2008
Consistency of Trace Norm Minimization JMLR 2008
Consistency of the Group Lasso and Multiple Kernel Learning JMLR 2008
DIFFRAC: a discriminative and flexible framework for clustering NIPS 2007
Statistical Consistency of Kernel Canonical Correlation Analysis JMLR 2007
Testing for Homogeneity with Kernel Fisher Discriminant Analysis NIPS 2007
Learning Spectral Clustering, With Application To Speech Separation JMLR 2006
Considering Cost Asymmetry in Learning Classifiers JMLR 2006
Active learning for misspecified generalized linear models NIPS 2006
Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces JMLR 2004
Beyond Independent Components: Trees and Clusters JMLR 2003
Kernel Independent Component Analysis JMLR 2002