Julien Mairal

Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching ICCV 2025 A New Statistical Model of Star Speckles for Learning to Detect and Characterize Exoplanets in Direct Imaging Observations CVPR 2025 LUDVIG: Learning-Free Uplifting of 2D Visual Features to Gaussian Splatting Scenes ICCV 2025 Vision Transformers Need Registers ICLR 2024 Functional Bilevel Optimization for Machine Learning NIPS 2024 Semi-Supervised Learning Made Simple With Self-Supervised Clustering CVPR 2023 SLACK: Stable Learning of Augmentations With Cold-Start and KL Regularization CVPR 2023 GloptiNets: Scalable Non-Convex Optimization with Certificates NIPS 2023 Sequential Counterfactual Risk Minimization ICML 2023 Self-Supervised Models Are Continual Learners CVPR 2022 Amortized Implicit Differentiation for Stochastic Bilevel Optimization ICLR 2022 The Spectral Bias of Polynomial Neural Networks ICLR 2022 Non-Convex Bilevel Games with Critical Point Selection Maps NIPS 2022 Efficient Kernelized UCB for Contextual Bandits AISTATS 2022 On the Benefits of Large Learning Rates for Kernel Methods COLT 2022 Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization NIPS 2021 A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention ICLR 2021 Emerging Properties in Self-Supervised Vision Transformers ICCV 2021 Lucas-Kanade Reloaded: End-to-End Super-Resolution From Raw Image Bursts ICCV 2021 A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration NIPS 2021 Convolutional Kernel Networks for Graph-Structured Data ICML 2020 Unsupervised Learning of Visual Features by Contrasting Cluster Assignments NIPS 2020 A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding NIPS 2020 Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise JMLR 2020 Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions AISTATS 2020 Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration ECCV 2020 Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification ECCV 2020 Diversity With Cooperation: Ensemble Methods for Few-Shot Classification ICCV 2019 A Generic Acceleration Framework for Stochastic Composite Optimization NIPS 2019 On the Inductive Bias of Neural Tangent Kernels NIPS 2019 Recurrent Kernel Networks NIPS 2019 Unsupervised Pre-Training of Image Features on Non-Curated Data ICCV 2019 A Kernel Perspective for Regularizing Deep Neural Networks ICML 2019 Estimate Sequences for Variance-Reduced Stochastic Composite Optimization ICML 2019 Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations JMLR 2019 Catalyst for Gradient-based Nonconvex Optimization AISTATS 2018 Unsupervised Learning of Artistic Styles with Archetypal Style Analysis NIPS 2018 Modeling Visual Context is Key to Augmenting Object Detection Datasets ECCV 2018 Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice JMLR 2018 BlitzNet: A Real-Time Deep Network for Scene Understanding ICCV 2017 Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure NIPS 2017 Invariance and Stability of Deep Convolutional Representations NIPS 2017 Learning Neural Representations of Human Cognition across Many fMRI Studies NIPS 2017 Dictionary Learning for Massive Matrix Factorization ICML 2016 End-to-End Kernel Learning with Supervised Convolutional Kernel Networks NIPS 2016 Local Convolutional Features With Unsupervised Training for Image Retrieval ICCV 2015 A Universal Catalyst for First-Order Optimization NIPS 2015 Convolutional Kernel Networks NIPS 2014 Fast and Robust Archetypal Analysis for Representation Learning CVPR 2014 Mixing Body-Part Sequences for Human Pose Estimation CVPR 2014 On learning to localize objects with minimal supervision ICML 2014 Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows JMLR 2013 Optimization with First-Order Surrogate Functions ICML 2013 Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization NIPS 2013 Structured Penalties for Log-Linear Language Models EMNLP 2013 Convex and Network Flow Optimization for Structured Sparsity JMLR 2011 Proximal Methods for Hierarchical Sparse Coding JMLR 2011 Online Learning for Matrix Factorization and Sparse Coding JMLR 2010 Network Flow Algorithms for Structured Sparsity NIPS 2010 Supervised Dictionary Learning NIPS 2008