← Authors

Francis Bach

124 papers · 2008–2025 · 13 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (13) πŸ—ΊοΈ Taxonomy Completionist (29) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (11) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (17) 🏠 Conference Loyalist (33) 🌟 Keyword Trendsetter Combo (7) 🐺 Lone Wolf (5) 🀝 Dynamic Duo (16) πŸ† Keyword Champion (2) πŸ”¬ Deep Specialist (50) πŸ—ƒοΈ Keyword Collector (118) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (18) πŸš€ Conference Pioneer ⚑ Prolific Year (9) ❓ The Questioner πŸ’Ž Century Club (124)

Conferences

NIPS (33) AISTATS (26) JMLR (21) ICML (18) COLT (16) CVPR (2) ICLR (2) COLING (1) CONLL (1) EMNLP (1) ICCV (1) IJCAI (1) INTERSPEECH (1)

Top co-authors

Alessandro Rudi (16) Simon Lacoste-Julien (7) Laurent MassouliΓ© (6) Jean-philippe Vert (5) Alexandre D'aspremont (5) Guillaume Obozinski (5) Nicolas Flammarion (5) Rodolphe Jenatton (5) LΓ©naΓ―c Chizat (4) Loucas Pillaud-Vivien (4)

Research topics

Statistics (1)

Keywords

convex optimization (21) convergence rate (12) stochastic gradient descent (11) kernel methods (10) stochastic optimization (9) gradient descent (8) structured prediction (8) neural network (7) convex relaxation (6) stochastic gradient (6) optimal transport (6) least squares regression (6) proximal operator (5) distributed optimization (4) independent component analysis (4) non-convex optimization (4) generalization bound (4) combinatorial optimization (4) dictionary learning (4) convergence analysis (4)

Papers

Optimal Denoising in Score-Based Generative Models: The Role of Data Regularity JMLR 2025
Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation JMLR 2025
Enhanced Feature Learning via Regularisation: Integrating Neural Networks and Kernel Methods JMLR 2025
Efficient Optimization Algorithms for Linear Adversarial Training AISTATS 2025
Variational Inference on the Boolean Hypercube with the Quantum Entropy AISTATS 2025
An uncertainty principle for Linear Recurrent Neural Networks COLT 2025
The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training ICML 2025
Physics-informed Kernel Learning JMLR 2025
Geometry-Dependent Matching Pursuit: a Transition Phase for Convergence on Linear Regression and LASSO JMLR 2025
Statistical Collusion by Collectives on Learning Platforms ICML 2025
Sampling Binary Data by Denoising through Score Functions ICML 2025
Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence NIPS 2024
Classifier Calibration with ROC-Regularized Isotonic Regression AISTATS 2024
Chain of Log-Concave Markov Chains ICLR 2024
Physics-informed machine learning as a kernel method COLT 2024
The Galerkin method beats Graph-Based Approaches for Spectral Algorithms AISTATS 2024
On the Impact of Overparameterization on the Training of a Shallow Neural Network in High Dimensions AISTATS 2024
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy ICML 2023
On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization ICML 2023
Kernelized Diffusion Maps COLT 2023
Regression as Classification: Influence of Task Formulation on Neural Network Features AISTATS 2023
Explicit Regularization in Overparametrized Models via Noise Injection AISTATS 2023
Sampling from Arbitrary Functions via PSD Models AISTATS 2022
Convergence of Uncertainty Sampling for Active Learning ICML 2022
On the Consistency of Max-Margin Losses AISTATS 2022
Anticorrelated Noise Injection for Improved Generalization ICML 2022
Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares COLT 2022
Deep Equals Shallow for ReLU Networks in Kernel Regimes ICLR 2021
Fast Rates for Structured Prediction COLT 2021
A Dimension-free Computational Upper-bound for Smooth Optimal Transport Estimation COLT 2021
Disambiguation of Weak Supervision leading to Exponential Convergence rates ICML 2021
Explicit Regularization of Stochastic Gradient Methods through Duality AISTATS 2021
Learning With Subquadratic Regularization : A Primal-Dual Approach IJCAI 2020
Statistical Estimation of the PoincarΓ© constant and Application to Sampling Multimodal Distributions AISTATS 2020
Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss COLT 2020
Consistent Structured Prediction with Max-Min Margin Markov Networks ICML 2020
Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization ICML 2020
Structured Prediction with Partial Labelling through the Infimum Loss ICML 2020
Stochastic Optimization for Regularized Wasserstein Estimators ICML 2020
Stochastic algorithms with descent guarantees for ICA AISTATS 2019
Sample Complexity of Sinkhorn Divergences AISTATS 2019
Sharp Analysis of Learning with Discrete Losses AISTATS 2019
Overcomplete Independent Component Analysis via SDP AISTATS 2019
Unsupervised Image Matching and Object Discovery as Optimization CVPR 2019
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization NIPS 2019
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses NIPS 2019
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums NIPS 2019
Localized Structured Prediction NIPS 2019
Optimal Convergence Rates for Convex Distributed Optimization in Networks JMLR 2019
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions COLT 2019
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance COLT 2019
A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise COLT 2019
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks NIPS 2019
Massively scalable Sinkhorn distances via the NystrΓΆm method NIPS 2019
Fast Decomposable Submodular Function Minimization using Constrained Total Variation NIPS 2019
Towards closing the gap between the theory and practice of SVRG NIPS 2019
On Lazy Training in Differentiable Programming NIPS 2019
Partially Encrypted Deep Learning using Functional Encryption NIPS 2019
Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives AISTATS 2019
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron AISTATS 2019
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes NIPS 2018
Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes NIPS 2018
SING: Symbol-to-Instrument Neural Generator NIPS 2018
Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization NIPS 2018
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks NIPS 2018
On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport NIPS 2018
Relating Leverage Scores and Density using Regularized Christoffel Functions NIPS 2018
Learning Determinantal Point Processes in Sublinear Time AISTATS 2018
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods AISTATS 2018
Convex Optimization over Intersection of Simple Sets: improved Convergence Rate Guarantees via an Exact Penalty Approach AISTATS 2018
A Generic Approach for Escaping Saddle points AISTATS 2018
Combinatorial Penalties: Which structures are preserved by convex relaxations? AISTATS 2018
Exponential Convergence of Testing Error for Stochastic Gradient Methods COLT 2018
Averaging Stochastic Gradient Descent on Riemannian Manifolds COLT 2018
Integration Methods and Optimization Algorithms NIPS 2017
A Quantitative Measure of the Impact of Coarticulation on Phone Discriminability INTERSPEECH 2017
Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks ICML 2017
Identifying Groups of Strongly Correlated Variables through Smoothed Ordered Weighted $L_1$-norms AISTATS 2017
Active-set Methods for Submodular Minimization Problems JMLR 2017
Kernel Square-Loss Exemplar Machines for Image Retrieval CVPR 2017
Nonlinear Acceleration of Stochastic Algorithms NIPS 2017
On Structured Prediction Theory with Calibrated Convex Surrogate Losses NIPS 2017
Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling JMLR 2017
Stochastic Composite Least-Squares Regression with Convergence Rate $O(1/n)$ COLT 2017
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression JMLR 2017
Robust Discriminative Clustering with Sparse Regularizers JMLR 2017
On the Consistency of Ordinal Regression Methods JMLR 2017
On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions JMLR 2017
Breaking the Curse of Dimensionality with Convex Neural Networks JMLR 2017
Parameter Learning for Log-supermodular Distributions NIPS 2016
Regularized Nonlinear Acceleration NIPS 2016
PAC-Bayesian Theory Meets Bayesian Inference NIPS 2016
Highly-Smooth Zero-th Order Online Optimization COLT 2016
Stochastic Variance Reduction Methods for Saddle-Point Problems NIPS 2016
Stochastic Optimization for Large-scale Optimal Transport NIPS 2016
Beyond CCA: Moment Matching for Multi-View Models ICML 2016
Weakly-Supervised Alignment of Video With Text ICCV 2015
From Averaging to Acceleration, There is Only a Step-size COLT 2015
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering AISTATS 2015
Averaged Least-Mean-Squares: Bias-Variance Trade-offs and Optimal Sampling Distributions AISTATS 2015
Rethinking LDA: Moment Matching for Discrete ICA NIPS 2015
Spectral Norm Regularization of Orthonormal Representations for Graph Transduction NIPS 2015
Adaptivity of Averaged Stochastic Gradient Descent to Local Strong Convexity for Logistic Regression JMLR 2014
Metric Learning for Temporal Sequence Alignment NIPS 2014
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives NIPS 2014
A Markovian approach to distributional semantics with application to semantic compositionality COLING 2014
Large-Margin Metric Learning for Constrained Partitioning Problems ICML 2014
Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs ICML 2013
Intersecting singularities for multi-structured estimation ICML 2013
Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression ICML 2013
Sharp analysis of low-rank kernel matrix approximations COLT 2013
Structured Penalties for Log-Linear Language Models EMNLP 2013
Hidden Markov tree models for semantic class induction CONLL 2013
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) NIPS 2013
Convex Relaxations for Permutation Problems NIPS 2013
Reflection methods for user-friendly submodular optimization NIPS 2013
Multi-task Regression using Minimal Penalties JMLR 2012
Convex and Network Flow Optimization for Structured Sparsity JMLR 2011
Structured Variable Selection with Sparsity-Inducing Norms JMLR 2011
Proximal Methods for Hierarchical Sparse Coding JMLR 2011
Online Learning for Matrix Factorization and Sparse Coding JMLR 2010
Structured Sparse Principal Component Analysis AISTATS 2010
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization JMLR 2009
Optimal Solutions for Sparse Principal Component Analysis JMLR 2008