Francis R. Bach

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

Achievements

+15 more ↓

🗺️ Taxonomy Completionist (29) 🧭 Keyword Pioneer 🌈 Renaissance Researcher (7) 🌉 Interdisciplinary Bridge 🐣 Hot Topic Early Bird

🧭 Keyword Pioneer 🌈 Renaissance Researcher (7) 🐣 Hot Topic Early Bird 🐺 Lone Wolf (6) 🏠 Conference Loyalist (37) 🌟 Keyword Trendsetter Combo (9) 🏆 Keyword Champion (8) 🌱 Topic Pioneer 🔬 Deep Specialist (10) 🗃️ Keyword Collector (127) 🚀 Conference Pioneer 📈 Trend Setter ⚡ Prolific Year (6) 💎 Century Club (46) 🔥 Unstoppable (7)

Conferences

NIPS (37) JMLR (9)

Top co-authors

Michael I. Jordan (5) Zaïd Harchaoui (3) Alessandro Rudi (3) Quentin Berthet (3) Hadi Daneshmand (3) Vivien Cabannes (2) Jean Ponce (2) Raphaël Berthier (2) Mark Schmidt (2) Laurent Massoulié (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