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Julien Mairal

60 papers · 2008–2025 · 10 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (17) 🌈 Renaissance Researcher (5) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (10) 🐝 Cross-Pollinator (14) πŸ—ΊοΈ Taxonomy Completionist (16) 🐺 Lone Wolf (3) 🏠 Conference Loyalist (20) πŸ† Keyword Champion (2) πŸ”¬ Deep Specialist (20) πŸ—ƒοΈ Keyword Collector (210) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (60) πŸ”₯ Unstoppable (13) ⚑ Prolific Year (5)

Conferences

NIPS (20) ICCV (8) ICML (7) JMLR (7) CVPR (6) ICLR (4) AISTATS (3) ECCV (3) COLT (1) EMNLP (1)

Top co-authors

Cordelia Schmid (8) ZaΓ―d Harchaoui (7) Michael Arbel (6) Alberto Bietti (6) Jean Ponce (6) Piotr Bojanowski (4) Francis Bach (4) Nikita Dvornik (4) Dexiong Chen (4) Rodolphe Jenatton (3)

Research topics

Core AI (1) Optimization & Theory (1)

Keywords

kernel methods (6) convex optimization (6) dictionary learning (6) image classification (6) sparse coding (6) accelerated gradient (5) reproducing kernel hilbert space (5) stochastic optimization (5) gradient descent (5) variance reduction (4) representation learning (4) unsupervised learning (4) self-supervised learning (4) convolutional neural network (4) matrix factorization (4) proximal operator (4) bilevel optimization (3) knowledge distillation (3) composite optimization (3) feature learning (3)

Papers

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