J. Mairal

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Optimization with Sparsity-Inducing Penalties (Foundations and Trends(R) in Machine Learning)

F. Bach, Rodolphe Jenatton, J. Mairal, 2011 .

Sparse coding for machine learning, image processing and computer vision

J. Mairal, 2010 .

Online dictionary learning for sparse coding

Guillermo Sapiro, Jean Ponce, Julien Mairal, 2009, ICML '09.

Online Learning for Matrix Factorization and Sparse Coding

Guillermo Sapiro, Jean Ponce, Julien Mairal, 2009, J. Mach. Learn. Res..

A Universal Catalyst for First-Order Optimization

Zaïd Harchaoui, Julien Mairal, Hongzhou Lin, 2015, NIPS.

Supervised Dictionary Learning

Guillermo Sapiro, Jean Ponce, Andrew Zisserman, 2008, NIPS.

Discriminative learned dictionaries for local image analysis

Guillermo Sapiro, Jean Ponce, Andrew Zisserman, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

On learning to localize objects with minimal supervision

Zaïd Harchaoui, Trevor Darrell, Julien Mairal, 2014, ICML.

Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation

Martial Hebert, Jean Ponce, Julien Mairal, 2008, ECCV.

Optimization with First-Order Surrogate Functions pdf

Julien Mairal, J. Mairal, 2013, ICML.

Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning

Julien Mairal, J. Mairal, 2014, SIAM J. Optim..

Optimization with Sparsity-Inducing Penalties pdf

Julien Mairal, Francis R. Bach, Guillaume Obozinski, 2011, Found. Trends Mach. Learn..

QuickeNing: A Generic Quasi-Newton Algorithm for Faster Gradient-Based Optimization

Z. Harchaoui, J. Mairal, Hongzhou Lin, 2016 .

Convex Sparse Matrix Factorizations pdf

Jean Ponce, Julien Mairal, Francis R. Bach, 2008, ArXiv.

Unsupervised Pre-Training of Image Features on Non-Curated Data

Julien Mairal, Armand Joulin, Piotr Bojanowski, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Convex optimization with sparsity-inducing norms

Julien Mairal, Guillaume Obozinski, Francis Bach, 2011 .

Task-Driven Dictionary Learning

Jean Ponce, Julien Mairal, Francis R. Bach, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Non-local sparse models for image restoration

Guillermo Sapiro, Jean Ponce, Andrew Zisserman, 2009, 2009 IEEE 12th International Conference on Computer Vision.

Proximal Methods for Sparse Hierarchical Dictionary Learning

Julien Mairal, Francis R. Bach, Guillaume Obozinski, 2010, ICML.

Learning Multiscale Sparse Representations for Image and Video Restoration

Michael Elad, Guillermo Sapiro, Julien Mairal, 2007, Multiscale Model. Simul..

Network Flow Algorithms for Structured Sparsity

Julien Mairal, Francis R. Bach, Guillaume Obozinski, 2010, NIPS.

Sparse Representation for Color Image Restoration

Michael Elad, Guillermo Sapiro, Julien Mairal, 2008, IEEE Transactions on Image Processing.

Convolutional Kernel Networks

Cordelia Schmid, Zaïd Harchaoui, Julien Mairal, 2014, NIPS.

Sparse Modeling for Image and Vision Processing pdf

Jean Ponce, Julien Mairal, Francis R. Bach, 2014, Found. Trends Comput. Graph. Vis..

Catalyst Acceleration for Gradient-Based Non-Convex Optimization

Z. Harchaoui, J. Mairal, D. Drusvyatskiy, 2017, 1703.10993.

Structured sparsity through convex optimization pdf

Julien Mairal, Francis R. Bach, Guillaume Obozinski, 2011, ArXiv.

Fast and Robust Archetypal Analysis for Representation Learning

Zaïd Harchaoui, Julien Mairal, Yuansi Chen, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

Images Instances Using context guidance Random instance placement Copy-Paste Data Augmentation New Training Examples

C. Schmid, J. Mairal, Nikita Dvornik, 2018 .

Loter: A Software Package to Infer Local Ancestry for a Wide Range of Species

J. Mairal, M. Blum, Thomas Dias-Alves, 2017, bioRxiv.

On the Inductive Bias of Neural Tangent Kernels

Julien Mairal, Alberto Bietti, J. Mairal, 2019, NeurIPS.

Loter: A software package to infer local ancestry for a wide range of species

J. Mairal, M. Blum, Thomas Dias-Alves, 2017, bioRxiv.

New Results - Learning and statistical models

Guillaume Bouchard, Julien Mairal, Bin Yu, 2013 .

Models of Visual Object Recognition and Scene Understanding

Jean-Philippe Pons, Jean Ponce, Y-Lan Boureau, 2007 .

New Results - Image restoration, manipulation and enhancement

Jean Ponce, Andrew Zisserman, Julien Mairal, 2011 .

Software and Platforms - SPArse Modeling Software (SPAMS)

Jean Ponce, Julien Mairal, Francis Bach, 2013 .

Learning for Massive Matrix Factorization

G. Varoquaux, B. Thirion, J. Mairal, 2017 .

Path Coding Penalties for Directed Acyclic Graphs

Bin Yu, J. Mairal, 2011 .

Finding Winning Tickets with Limited (or No) Supervision

Ari S. Morcos, J. Mairal, Armand Joulin, 2019 .

New Results - Visual recognition in images

Cordelia Schmid, Trevor Darrell, C. V. Jawahar, 2013 .

New Software and Platforms - Convolutional Kernel Networks

Cordelia Schmid, Julien Mairal, Piotr Koniusz, 2014 .

A GPU implementation of variational stereo Implémentation d ’ un algorithme de stéréovision variationnel sur GPU

J. Mairal, R. Keriven, Alexandre Chariot, 2005 .

GraphiT: Encoding Graph Structure in Transformers pdf

Julien Mairal, Dexiong Chen, Gr'egoire Mialon, 2021, ArXiv.

Subsampling Enables Fast Factorisation of Huge Matrices into Sparse Signals

Gaël Varoquaux, Bertrand Thirion, Arthur Mensch, 2017, ERCIM News.

Extracting Universal Representations of Cognition across Brain-Imaging Studies pdf

Gaël Varoquaux, Bertrand Thirion, Arthur Mensch, 2018, ArXiv.

Extracting representations of cognition across neuroimaging studies improves brain decoding

Gaël Varoquaux, Bertrand Thirion, Arthur Mensch, 2018, PLoS Comput. Biol..

Self-Supervised Models are Continual Learners (Supplementary Material)

Alahari Karteek, J. Mairal, E. Ricci, 2022 .

Amortized Implicit Differentiation for Stochastic Bilevel Optimization

J. Mairal, M. Arbel, 2021, ICLR.

Exoplanet detection in angular differential imaging: combining a statistics-based learning with a deep-based learning for improved detections

J. Ponce, J. Mairal, A. Lagrange, 2022, Astronomical Telescopes + Instrumentation.

Learning Reward Functions for Robotic Manipulation by Observing Humans

J. Ponce, C. Schmid, J. Mairal, 2022, 2211.09019.

Self Supervised Learning for Few Shot Hyperspectral Image Classification

Xiao Xiang Zhu, J. Mairal, J. Chanussot, 2022, IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium.

On the Benefits of Large Learning Rates for Kernel Methods

J. Mairal, Alessandro Rudi, Gaspard Beugnot, 2022, COLT.

Biological Sequence Modeling with Convolutional Kernel Networks

Julien Mairal, Dexiong Chen, Laurent Jacob, 2018, bioRxiv.

Counterfactual Learning of Stochastic Policies with Continuous Actions: from Models to Offline Evaluation

J. Mairal, A. Bietti, Houssam Zenati, 2020, 2004.11722.

Fully Trainable and Interpretable Non-local Sparse Models for Image Restoration

J. Ponce, J. Mairal, Bruno Lecouat, 2019, ECCV.

Revisiting Non Local Sparse Models for Image Restoration pdf

Jean Ponce, Julien Mairal, Bruno Lecouat, 2019, ArXiv.

Stochastic Subsampling for Factorizing Huge Matrices

Gaël Varoquaux, Bertrand Thirion, Arthur Mensch, 2017, IEEE Transactions on Signal Processing.

Subsampled online matrix factorization with convergence guarantees

Bertrand Thirion, Gael Varoquaux, Arthur Mensch, 2016, NIPS 2016.

Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization

Julien Mairal, J. Mairal, 2013, NIPS.

Dictionary Learning for Deblurring and Digital Zoom pdf

Jean Ponce, Julien Mairal, Francis R. Bach, 2011, ArXiv.

Learning hierarchical and topographic dictionaries with structured sparsity

Julien Mairal, Francis R. Bach, Guillaume Obozinski, 2011, Optical Engineering + Applications.

Sparse image representation with epitomes

Jean Ponce, Julien Mairal, Francis Bach, 2011, CVPR 2011.

Proximal Methods for Hierarchical Sparse Coding

Julien Mairal, Francis R. Bach, Guillaume Obozinski, 2010, J. Mach. Learn. Res..

Sparse Representation for Computer Vision and Pattern Recognition

Guillermo Sapiro, Shuicheng Yan, Thomas S. Huang, 2010, Proceedings of the IEEE.

Sparse Representation forComputerVision and Pattern Recognition A relatively small sample of computer vision and pattern recognition information in applications such as face recognition is often sufficient to reveal the meaning the user desires.

Guillermo Sapiro, Shuicheng Yan, Thomas S. Huang, 2010 .

Recent Advances in Structured Sparse Models

Julien Mairal, J. Mairal, 2010 .

Aliasing is your Ally: End-to-End Super-Resolution from Raw Image Bursts pdf

Jean Ponce, Julien Mairal, Bruno Lecouat, 2021, ArXiv.

Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts

Jean Ponce, Julien Mairal, Bruno Lecouat, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise

Julien Mairal, Andrei Kulunchakov, J. Mairal, 2019, J. Mach. Learn. Res..

A Generic Acceleration Framework for Stochastic Composite Optimization

Julien Mairal, Andrei Kulunchakov, J. Mairal, 2019, NeurIPS.

An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration

Julien Mairal, Zaid Harchaoui, Hongzhou Lin, 2016, SIAM J. Optim..

Catalyst for Gradient-based Nonconvex Optimization

Zaïd Harchaoui, Dmitriy Drusvyatskiy, Julien Mairal, 2018, AISTATS.

Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice

Zaïd Harchaoui, Julien Mairal, Hongzhou Lin, 2017, J. Mach. Learn. Res..

Mairal Large-Scale Machine Learning and Applications Soutenue le 4 octobre 2017

J. Mairal, 2017 .

Emerging Properties in Self-Supervised Vision Transformers pdf

Julien Mairal, Armand Joulin, Piotr Bojanowski, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Structured Penalties for Log-Linear Language Models

Guillaume Bouchard, Julien Mairal, Cédric Archambeau, 2013, EMNLP.

Efficient RNA isoform identification and quantification from RNA-Seq data with network flows

Julien Mairal, Jean-Philippe Vert, Laurent Jacob, 2014, Bioinform..

RNA Isoform Identication and Quantication from RNA-Seq Data with Network Flows

Julien Mairal, Jean-Philippe Vert, Laurent Jacob, 2014 .

Supervised feature selection in graphs with path coding penalties and network flows pdf

Julien Mairal, Bin Yu, Bin Yu, 2012, J. Mach. Learn. Res..

FlipFlop: Fast Lasso-based Isoform Prediction as a Flow Problem

Julien Mairal, Jean-Philippe Vert, Laurent Jacob, 2013 .

Convex and Network Flow Optimization for Structured Sparsity pdf

Julien Mairal, Francis R. Bach, Guillaume Obozinski, 2011, J. Mach. Learn. Res..

Mixing Body-Part Sequences for Human Pose Estimation

Cordelia Schmid, Anoop Cherian, Julien Mairal, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments pdf

Julien Mairal, Armand Joulin, Piotr Bojanowski, 2020, NeurIPS.

APPLES: Fast Distance-Based Phylogenetic Placement

Larry Smarr, Andrey D. Prjibelski, Pavel A. Pevzner, 2019, RECOMB.

Local Convolutional Features with Unsupervised Training for Image Retrieval

Cordelia Schmid, Matthijs Douze, Zaïd Harchaoui, 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

Convolutional Patch Representations for Image Retrieval: An Unsupervised Approach

Cordelia Schmid, Matthijs Douze, Zaïd Harchaoui, 2016, International Journal of Computer Vision.

A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding

Jean Ponce, Julien Mairal, Bruno Lecouat, 2020, NeurIPS.

Designing and Learning Trainable Priors with Non-Cooperative Games pdf

Jean Ponce, Julien Mairal, Bruno Lecouat, 2020, ArXiv.

Learning Reward Functions for Robotic Manipulation by Observing Humans

J. Ponce, C. Schmid, J. Mairal, 2022, ArXiv.

Biological Sequence Modeling with Convolutional Kernel Networks

Julien Mairal, Dexiong Chen, Laurent Jacob, 2017, bioRxiv.

Modeling Visual Context is Key to Augmenting Object Detection Datasets

Cordelia Schmid, Julien Mairal, Nikita Dvornik, 2018, ECCV.

Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C++, and soon more pdf

Julien Mairal, J. Mairal, 2019, ArXiv.

DINOv2: Learning Robust Visual Features without Supervision

Michael G. Rabbat, Huy Q. Vo, Nicolas Ballas, 2023, ArXiv.

C ONTINUOUS P SEUDO -L ABELING FROM THE S TART REPRINT

H. Jégou, J. Mairal, Piotr Bojanowski, 2022 .

BlitzNet: A Real-Time Deep Network for Scene Understanding

Julien Mairal, Cordelia Schmid, Nikita Dvornik, 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

Semi-supervised learning made simple with self-supervised clustering

Alahari Karteek, J. Mairal, E. Ricci, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Self-Supervised Models are Continual Learners

Alahari Karteek, J. Mairal, E. Ricci, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

33 32 v 2 [ cs . C V ] 14 N ov 2 01 4 Convolutional Kernel Networks

C. Schmid, Z. Harchaoui, J. Mairal, 2021 .

Complexity Analysis of the Lasso Regularization Path pdf

Julien Mairal, Bin Yu, Bin Yu, 2012, ICML.

Estimate Sequences for Variance-Reduced Stochastic Composite Optimization

Julien Mairal, Andrei Kulunchakov, J. Mairal, 2019, ICML.

End-to-End Kernel Learning with Supervised Convolutional Kernel Networks

Julien Mairal, J. Mairal, 2016, NIPS.

NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results

Radu Timofte, Jean Ponce, Christian Micheloni, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration pdf

Jocelyn Chanussot, Julien Mairal, Th'eo Bodrito, 2021, NeurIPS.

Non-Convex Bilevel Games with Critical Point Selection Maps

J. Mairal, M. Arbel, 2022, NeurIPS.

Extracting representations of cognition across neuroimaging studies improves brain decoding

G. Varoquaux, B. Thirion, J. Mairal, 2018, PLoS Comput. Biol..

Selecting Relevant Features from a Multi-domain Representation for Few-Shot Classification

Julien Mairal, Cordelia Schmid, Nikita Dvornik, 2020, ECCV.

Diversity With Cooperation: Ensemble Methods for Few-Shot Classification

Cordelia Schmid, Julien Mairal, Nikita Dvornik, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Dictionary learning from phaseless measurements

Yonina C. Eldar, Andreas M. Tillmann, Julien Mairal, 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

DOLPHIn—Dictionary Learning for Phase Retrieval

Yonina C. Eldar, Andreas M. Tillmann, Julien Mairal, 2016, IEEE Transactions on Signal Processing.

On Regularization and Robustness of Deep Neural Networks pdf

Julien Mairal, Alberto Bietti, Grégoire Mialon, 2018, ArXiv.

Invariance and Stability of Deep Convolutional Representations

Julien Mairal, Alberto Bietti, J. Mairal, 2017, NIPS.

Beyond Tikhonov: Faster Learning with Self-Concordant Losses via Iterative Regularization pdf

Alessandro Rudi, Julien Mairal, Gaspard Beugnot, 2021, NeurIPS.

Selecting Relevant Features from a Universal Representation for Few-shot Classification

C. Schmid, J. Mairal, Nikita Dvornik, 2020, ECCV 2020.

An Optimal Transport Kernel for Feature Aggregation and its Relationship to Attention

J. Mairal, Dexiong Chen, Grégoire Mialon, 2020, ArXiv.

Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing

Inria, J. Mairal, J. Chanussot, 2022, ArXiv.

Self-Attention in Colors: Another Take on Encoding Graph Structure in Transformers

J. Mairal, Margot Selosse, Emmanuel Jehanno, 2023, ArXiv.

Learning Neural Representations of Human Cognition across Many fMRI Studies

Thomas E. Nichols, Danilo Bzdok, Gaël Varoquaux, 2017, NIPS.

Convolutional Kernel Networks for Graph-Structured Data pdf

Julien Mairal, Dexiong Chen, Laurent Jacob, 2020, ICML.

On the Importance of Visual Context for Data Augmentation in Scene Understanding

Cordelia Schmid, Julien Mairal, Nikita Dvornik, 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure

Julien Mairal, Alberto Bietti, J. Mairal, 2016, NIPS.

Pruning Convolutional Neural Networks with Self-Supervision pdf

Julien Mairal, Armand Joulin, Piotr Bojanowski, 2020, ArXiv.

Leveraging Large-Scale Uncurated Data for Unsupervised Pre-training of Visual Features pdf

Julien Mairal, Armand Joulin, Piotr Bojanowski, 2019, ArXiv.

Network Flow Algorithms for Structured Sparsity

G. Obozinski, F. Bach, Rodolphe Jenatton, 2010, NIPS.

A Kernel Perspective for Regularizing Deep Neural Networks

Julien Mairal, Dexiong Chen, Alberto Bietti, 2018, ICML.

Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations

Julien Mairal, Alberto Bietti, J. Mairal, 2017, J. Mach. Learn. Res..

Sequential Counterfactual Risk Minimization

J. Mairal, Houssam Zenati, Matthieu Martin, 2023, ICML.

Efficient Kernelized UCB for Contextual Bandits

J. Mairal, A. Bietti, Houssam Zenati, 2022, AISTATS.

The Spectral Bias of Polynomial Neural Networks

Grigorios G. Chrysos, V. Cevher, J. Mairal, 2022, ICLR.

High dynamic range and super-resolution from raw image bursts

J. Ponce, J. Mairal, Bruno Lecouat, 2022, ACM Trans. Graph..

Dictionary Learning for Massive Matrix Factorization pdf

Gaël Varoquaux, Bertrand Thirion, Arthur Mensch, 2016, ICML.

Dictionary Learning for Massive Matrix Factorization Dictionary Learning for Massive Matrix Factorization

G. Varoquaux, B. Thirion, J. Mairal, 2022 .

Sparse learned representations for image restoration

Michael Elad, Guillermo Sapiro, Julien Mairal, 2008 .

Counterfactual Learning of Continuous Stochastic Policies

J. Mairal, A. Bietti, Houssam Zenati, 2020 .

Group Invariance and Stability to Deformations of Deep Convolutional Representations pdf

Julien Mairal, Alberto Bietti, J. Mairal, 2017, ArXiv.

Supplementary Material for Unsupervised Pre-Training of Image Features on Non-Curated Data

J. Mairal, Armand Joulin, Piotr Bojanowski, 2019 .

Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package

J. Mairal, J. Chanussot, Behnood Rasti, 2023, ArXiv.

Supplementary Material for Emerging Properties in Self-Supervised Vision Transformers

H. Jégou, J. Mairal, Armand Joulin, 2021 .

A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention

Alexandre d'Aspremont, Julien Mairal, Dexiong Chen, 2020, ICLR.

Efficient RNA isoform identification and quantification from RNA-Seq data with network flows

J. Mairal, Jean-Philippe Vert, Laurent Jacob, 2014, Bioinform..

A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples

J. Mairal, Jean-Philippe Vert, Laurent Jacob, 2015, BMC Bioinformatics.

Residual Reinforcement Learning from Demonstrations pdf

Cordelia Schmid, Jean Ponce, Julien Mairal, 2021, ArXiv.

Efficient Kernel UCB for Contextual Bandits

J. Mairal, P. Gaillard, A. Bietti, 2022, ArXiv.

Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning

J. Mairal, 2014, SIAM Journal on Optimization.

Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization

J. Mairal, 2013, NIPS.

Estimate Sequences for Variance-Reduced Stochastic Composite Optimization

J. Mairal, A. Kulunchakov, 2019, ICML.

QuickeNing: A Generic Quasi-Newton Algorithm for Faster Gradient-Based Optimization

J. Mairal, Hongzhou Lin, Zaïd Harchaoui, 2016 .

Efficient RNA isoform identification and quantification from RNA-Seq data with network flows

J. Mairal, Jean-Philippe Vert, Laurent Jacob, 2014, Bioinform..

Recurrent Kernel Networks

Julien Mairal, Dexiong Chen, Laurent Jacob, 2019, NeurIPS.

Supervised feature selection in graphs with path coding penalties and network flows

Bin Yu, J. Mairal, 2012, J. Mach. Learn. Res..

Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package

J. Mairal, J. Chanussot, Behnood Rasti, 2023, IEEE Transactions on Geoscience and Remote Sensing.

SUnAA: Sparse Unmixing Using Archetypal Analysis

J. Mairal, J. Chanussot, Behnood Rasti, 2023, IEEE Geoscience and Remote Sensing Letters.

Local Convolutional Features with Unsupervised Training for Image Retrieval

F. Perronnin, C. Schmid, J. Mairal, 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

Priya Goyal, J. Mairal, Armand Joulin, 2020, NeurIPS.

Sparse image representation with epitomes

J. Mairal, Louise Benoît, Francis R. Bach, 2011, Computer Vision and Pattern Recognition.

Convex and Network Flow Optimization for Structured Sparsity

G. Obozinski, F. Bach, Rodolphe Jenatton, 2011, J. Mach. Learn. Res..

Task-Driven Dictionary Learning

F. Bach, J. Ponce, J. Mairal, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Proximal Methods for Hierarchical Sparse Coding

G. Obozinski, F. Bach, Rodolphe Jenatton, 2010, J. Mach. Learn. Res..

SUPERVISED DICTIONARY LEARNING By

J. Ponce, Andrew Zisserman, G. Sapiro, 2008 .

Supervised Dictionary Learning

J. Mairal, A. Zisserman, Jean Ponce, 2008 .

Non-Convex Bilevel Games with Critical Point Selection Maps

J. Mairal, M. Arbel, 2022, NeurIPS.

A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding

J. Ponce, J. Mairal, Bruno Lecouat, 2020, NeurIPS.

GloptiNets: Scalable Non-Convex Optimization with Certificates

J. Mairal, Alessandro Rudi, Gaspard Beugnot, 2023, NeurIPS.

Vision Transformers Need Registers

J. Mairal, Piotr Bojanowski, Timoth'ee Darcet, 2023, ArXiv.

Convolutional Kernel Networks for Graph-Structured Data

J. Mairal, Laurent Jacob, Dexiong Chen, 2020, ICML.

Fine Dense Alignment of Image Bursts through Camera Pose and Depth Estimation

J. Mairal, Bruno Lecouat, Y. Mont-Marin, 2023, 2312.05190.

Online dictionary learning for sparse coding

F. Bach, J. Ponce, G. Sapiro, 2009, ICML '09.

On Good Practices for Task-Specific Distillation of Large Pretrained Models

J. Mairal, Juliette Marrie, Michael Arbel, 2024, ArXiv.

Network Flow Algorithms for Structured Sparsity

G. Obozinski, F. Bach, Rodolphe Jenatton, 2010, NIPS.

Functional Bilevel Optimization for Machine Learning

J. Mairal, Michael Arbel, Ieva Petrulionyte, 2024, ArXiv.

Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Function

J. Mairal, Grégoire Mialon, A. d’Aspremont, 2019, AISTATS.

End-to-End Kernel Learning with Supervised Convolutional Kernel Networks

J. Mairal, 2016, NIPS.