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Julien Mairal - Publications by Year
My publications are also available on my Google Scholar profile.
2024
J. Marrie, R. Menegaux, M. Arbel, D. Larlus and J. Mairal. LUDVIG: Learning-free Uplifting of 2D Visual features to Gaussian Splatting scenes. preprint arXiv:2410.14462. 2024.
I. Petrulionyte, J. Mairal and M. Arbel. Functional Bilevel Optimization for Machine Learning. to appear in Adv. Neural Information Processing Systems (NeurIPS). 2024.
T. Bodrito, O. Flasseur, J. Mairal, J. Ponce, M. Langlois and A.-M. Lagrange. MODEL&CO: Exoplanet detection in angular differential imaging by learning across multiple observations. Monthly Notices of the Royal Astronomical Society (MNRAS). 2024.
J. Marrie, M. Arbel, J. Mairal and D. Larlus. On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models. Transactions on Machine Learning Research (TMLR). 2024. Outstanding paper finalist, TMLR 2024
B. Rasti, A. Zouaoui, J. Mairal and J. Chanussot. Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package. IEEE Transactions on Geoscience and Remote Sensing (TGRS). 2024.
T. Darcet, M. Oquab, J. Mairal and P. Bojanowski. Vision Transformers Need Registers. International Conference on Learning Representations (ICLR). 2024. Outstanding paper award, ICLR 2024.
O. Flasseur, T. Bodrito, J. Mairal, J. Ponce, M. Langlois and A.-M. Lagrange. Deep PACO: Combining Statistical Models with Deep Learning for Exoplanet Detection and Characterization in Direct Imaging at High Contrast. Monthly Notices of the Royal Astronomical Society (MNRAS). volume 527, issue 1, pages 1534-1562. 2024.
M. Oquab et al. DINOv2: Learning Robust Visual Features without Supervision. Transactions on Machine Learning Research (TMLR). 2024.
N. Ait Ali Braham, J. Mairal, J. Chanussot, L. Mou, X. X. Zhu. Enhancing Contrastive Learning with Positive Pair Mining for Few-shot Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS). 2024.
B. Rasti, A. Zouaoui, J. Mairal and J. Chanussot Fast Semi-supervised Unmixing using Non-convex Optimization. IEEE Transactions on Geoscience and Remote Sensing (TGRS). 2024.
2023
B. Lecouat, Y. Dubois de Mont-Marin, T. Bodrito, J. Mairal and J. Ponce. Fine Dense Alignment of Image Bursts through Camera Pose and Depth Estimation. preprint arXiv:2312:05190. 2023.
G. Beugnot, J. Mairal and A. Rudi. GloptiNets: Scalable Non-Convex Optimization with Certificates. Adv. Neural Information Processing Systems (NeurIPS). 2023.
R. Menegaux, E. Jehanno, M. Selosse and J. Mairal. Self-Attention in Colors: Another Take on Encoding Graph Structure in Transformers. Transactions on Machine Learning Research (TMLR). 2023.
O. Flasseur, T. Bodrito, J. Mairal, J. Ponce, M. Langlois and A.-M. Lagrange. Combining multi-spectral data with statistical and deep-learning models for improved exoplanet detection in direct imaging at high contrast. European Signal Processing Conference (EUSIPCO). 2023.
A. Zouaoui, G. Muhawenayo, B. Rasti, J. Chanussot and J. Mairal. Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing. IEEE Transactions on Image Processing. 2023.
B. Rasti, A. Zouaoui, J. Mairal and J. Chanussot. SUnAA: Sparse Unmixing using Archetypal Analysis IEEE Geoscience and Remote Sensing Letters. 2023.
H. Zenati, E. Diemert, M. Martin, J. Mairal and P. Gaillard. Sequential Counterfactual Risk Minimization. International Conference on Machine Learning (ICML). 2023.
J. Marrie, M. Arbel, D. Larlus and J. Mairal. SLACK: Stable Learning of Augmentations with Cold-start and KL regularization. International Conference on Computer Vision and Pattern Recognition (CVPR). 2023.
E. Fini, P. Astolfi, K. Alahari, X. Alameda-Pineda, J. Mairal, M. Nabi and E. Ricci. Semi-supervised learning made simple with self-supervised clustering. International Conference on Computer Vision and Pattern Recognition (CVPR). 2023.
M. Alakuijala, G. Dulac-Arnold, J. Mairal, J. Ponce and C. Schmid. Learning Reward Functions for Robotic Manipulation by Observing Humans. IEEE International Conference on Robotics and Automation (ICRA). 2023.
2022
M. Arbel and J. Mairal. Non-Convex Bilevel Games with Critical Point Selection Maps. Adv. Neural Information Processing Systems (NeurIPS). 2022.
B. Lecouat, T. Eboli, J. Ponce and J. Mairal. High Dynamic Range and Super-Resolution From Raw Image Bursts. ACM SIGGRAPH. 2022.
G. Beugnot, J. Mairal, and A. Rudi. On the Benefits of Large Learning Rates for Kernel Methods. International Conference on Learning Theory (COLT). 2022.
E. Fini, V. G. Turrisi da Costa, X. Alameda-Pineda, E. Ricci, K. Alahari and J. Mairal. Self-Supervised Models are Continual Learners. International Conference on Computer Vision and Pattern Recognition (CVPR). 2022.
M. Arbel and J. Mairal. Amortized Implicit Differentiation for Stochastic Bilevel Optimization. International Conference on Learning Representations (ICLR). 2022.
M. Choraria, L. T. Dadi, G. Chrysos, J. Mairal and V. Cevher. The Spectral Bias of Polynomial Neural Networks. International Conference on Learning Representations (ICLR). 2022.
H. Zenati, A. Bietti, E. Diemert, J. Mairal, M. Martin and P. Gaillard. Efficient Kernel UCB for Contextual Bandits. International Conference on Artificial Intelligence and Statistics (AISTATS). 2022.
2021
H. Zenati, A. Bietti, M. Martin, E. Diemert and J. Mairal. Counterfactual Learning of Stochastic Policies with Continuous Actions: from Models to Offline Evaluation. preprint arXiv:2004.11722. 2021. source code
M. Alakuijala, G. Dulac-Arnold, J. Mairal, J. Ponce, and C. Schmid. Residual Reinforcement Learning from Demonstrations. preprint arXiv:2106.08050. 2021.
G. Mialon, D. Chen, M. Selosse, and J. Mairal. GraphiT: Encoding Graph Structure in Transformers. preprint arXiv:2106.05667. 2021. source code
T. Bodrito, A. Zouaoui, J. Chanussot and J. Mairal. A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration. Adv. Neural Information Processing Systems (NeurIPS). 2021. source code
G. Beugnot, J. Mairal, and A. Rudi. Beyond Tikhonov: Faster Learning with Self-Concordant Losses via Iterative Regularization. Adv. Neural Information Processing Systems (NeurIPS). 2021.
M. Caron, H. Touvron, I. Misra, H. Jegou, J. Mairal, P. Bojanowski and A. Joulin. Emerging Properties in Self-Supervised Vision Transformers. International Conference on Computer Vision (ICCV). 2021. source code
B. Lecouat, J. Ponce and J. Mairal. Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts. International Conference on Computer Vision (ICCV). 2021.
G. Mialon, D. Chen, A. d'Aspremont and J. Mairal. A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention. International Conference on Learning Representations (ICLR). 2021. source code
A. Mensch, J. Mairal, B. Thirion and G. Varoquaux. Extracting representations of cognition across neuroimaging studies improves brain decoding. PLOS Computational Biology. 2021. source code
2020
B. Lecouat, J. Ponce and J. Mairal. Designing and Learning Trainable Priors with Non-Cooperative Games. Adv. Neural Information Processing Systems (NeurIPS). 2020. source code
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, A. Joulin. Unsupervised Learning of Visual Features by Contrasting Cluster Assignments. Adv. Neural Information Processing Systems (NeurIPS). 2020. source code
A. Kulunchakov and J. Mairal. Estimate Sequences for Stochastic Composite Optimization:
Variance Reduction, Acceleration, and Robustness to Noise. Journal of Machine Learning Research (JMLR) 21(155), pages 1–52, 2020.
B. Lecouat, J. Ponce and J. Mairal. Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration. European Conference on Computer Vision (ECCV). 2020. source code
N. Dvornik, C. Schmid and J. Mairal. Selecting Relevant Features from a Multi-Domain Representation for Few-shot Classification.
European Conference on Computer Vision (ECCV). 2020. source code
M. Caron, A. Morcos, P. Bojanowski, J. Mairal and A. Joulin. Pruning Convolutional Neural Networks with Self-Supervision. preprint arXiv:2001.03554. 2020.
D. Chen, L. Jacob and J. Mairal. Convolutional Kernel Networks for Graph-Structured Data. International Conference on Machine Learning (ICML). 2020. source code
G. Mialon, A. d'Aspremont and J. Mairal. Screening Data Points in Empirical Risk Minimization via
Ellipsoidal Regions and Safe Loss Functions. International Conference on Artificial Intelligence and Statistics (AISTATS). 2020. source code
2019
J. Mairal. Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C, and soon more. arXiv.1912.08165. 2019. source code
M. Dvornik, J. Mairal and C. Schmid. On the Importance of Visual Context for Data Augmentation in Scene Understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). 2019. source code
D. Chen, L. Jacob and J. Mairal. Recurrent Kernel Networks. Adv. Neural Information Processing Systems (NeurIPS). 2019. source code
A. Kulunchakov and J. Mairal. A Generic Acceleration Framework for Stochastic Composite Optimization. Adv. Neural Information Processing Systems (NeurIPS). 2019.
A. Bietti and J. Mairal. On the Inductive Bias of Neural Tangent Kernels. Adv. Neural Information Processing Systems (NeurIPS). 2019.
N. Dvornik, C. Schmid and J. Mairal. Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. International Conference on Computer Vision (ICCV). 2019. source code
M. Caron, P. Bojanowski, J. Mairal and A. Joulin. Unsupervised Pre-Training of Image Features on Non-Curated Data. International Conference on Computer Vision (ICCV). 2019. source code
A. Kulunchakov and J. Mairal. Estimate Sequences for Variance-Reduced Stochastic Composite Optimization. International Conference on Machine Learning (ICML). 2019.
A. Bietti, G. Mialon, D. Chen, and J. Mairal. A Kernel Perspective for Regularizing Deep Neural Networks. International Conference on Machine Learning (ICML). 2019. source code
D. Chen, L. Jacob, and J. Mairal. Biological Sequence Modeling with Convolutional Kernel Networks. Bioinformatics, volume 35, issue 18, pages 3294-3302, 2019. also accepted at RECOMB 2019. source code
H. Lin, J. Mairal and Z. Harchaoui. An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration. SIAM Journal on Optimization. 29(2), pages 1408–1443, 2019. source code
A. Bietti and J. Mairal. Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations. Journal of Machine Learning Research (JMLR). 20(25), pages 1–49, 2019.
2018
C. Paquette, H. Lin, D. Drusvyatskiy, J. Mairal, Z. Harchaoui. Catalyst Acceleration for Gradient-Based Non-Convex Optimization. preprint arXiv:1703.10993. 2018. (long version of the AISTATS paper below).
D. Wynen, C. Schmid and J. Mairal.
Unsupervised Learning of Artistic
Styles with Archetypal Style Analysis. Adv. Neural Information Processing Systems (NeurIPS). 2018. project page
M. Dvornik, J. Mairal and C. Schmid. Modeling Visual Context is Key to Augmenting Object Detection Datasets. European Conference on Computer Vision (ECCV). 2018. source code
T. Dias-Alves, J. Mairal, and M. Blum. Loter: A Software Package to Infer Local Ancestry for a Wide Range of Species. Molecular Biology and Evolution (MBE), volume 35, issue 9, pages 2318–2326, 2018. source code .
H. Lin, J. Mairal and Z. Harchaoui. Catalyst Acceleration for First-order
Convex Optimization: from Theory to Practice. Journal of Machine Learning Research (JMLR). 18(212), pages 1–54, 2018. source code
C. Paquette, H. Lin, D. Drusvyatskiy, J. Mairal, Z. Harchaoui. Catalyst for Gradient-Based Non-Convex Optimization. International Conference on Artificial Intelligence and Statistics (AISTATS). 2018.
A. Mensch, J. Mairal, B. Thirion and G. Varoquaux. Stochastic Subsampling for Factorizing Huge Matrices. IEEE Transactions on Signal Processing. 66(1), pages 113–128, 2018. source code .
2017
A. Bietti and J. Mairal. Invariance and Stability of Deep Convolutional Representations. Adv. Neural Information Processing Systems (NIPS). 2017.
A. Bietti and J. Mairal. Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite-Sum Structure. Adv. Neural Information Processing Systems (NIPS). 2017. source code
A. Mensch, J. Mairal, D. Bzok, B. Thirion and G. Varoquaux. Learning Neural Representations of Human Cognition across Many fMRI Studies. Adv. Neural Information Processing Systems (NIPS). 2017. source code
J. Mairal. Large-Scale Machine Learning and Applications. Mémoire d'habilitation à diriger des recherches. Univ. Grenoble-Alpes. 2017.
N. Dvornik, K. Shmelkov, J. Mairal and C. Schmid. BlitzNet: A Real-Time Deep Network for Scene Understanding. International Conference on Computer Vision (ICCV), 2017. source code
M. Paulin, J. Mairal, M. Douze, Z. Harchaoui, F. Perronnin, and C. Schmid. Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach. International Journal of Computer Vision (IJCV). 121(1), pages 149–168, 2017. project page + source code.
2016
J. Mairal. End-to-End Kernel Learning with Supervised Convolutional Kernel Networks. Adv. Neural Information Processing Systems (NIPS), 2016. source code . Errata .
A. Mensch, J. Mairal, B. Thirion and G. Varoquaux. Dictionary Learning for Massive Matrix Factorization. International Conference on Machine Learning (ICML), 2016. source code.
A. Tillmann, Y. C. Eldar, and J. Mairal. DOLPHIn-Dictionary Learning for Phase Retrieval. IEEE Transactions on Signal Processing, 64(24), pages 6485–6500, 2016. source code.
A. Tillmann, Y. C. Eldar, and J. Mairal. Dictionary Learning from Phaseless Measurements. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2016. source code.
2015
H. Lin, J. Mairal, and Z. Harchaoui. A Universal Catalyst for First-Order Optimization. Adv. Neural Information Processing Systems (NIPS). 2015.
M. Paulin, M. Douze, Z. Harchaoui, J. Mairal, F. Perronnin, and C. Schmid. Local Convolutional Features with Unsupervised Training for Image Retrieval. International Conference on Computer Vision (ICCV), 2015. project page + source code.
E. Bernard, L. Jacob, J. Mairal, E. Viara, and J-P. Vert. A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples. BMC Bioinformatics, volume 16, pages 262, 2015. source code.
J. Mairal, M. Elad and F. Bach. Guest Editorial: Sparse Coding.
International Journal of Computer Vision (IJCV). 114(2-3), pages 89-90. 2015
J. Mairal. Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning. SIAM Journal on Optimization. volume 25, number 2, pages 829–855, 2015. source code. scripts for reproducing the figures.
2014
J. Mairal, F. Bach and J. Ponce. Sparse Modeling for Image and Vision Processing. Foundations and Trends in Computer Graphics and Vision. volume 8(2-3), pages 85–283, 2014. (project page and software coming soon).
J. Mairal, P. Koniusz, Z. Harchaoui and C. Schmid. Convolutional Kernel Networks. Adv. Neural Information Processing Systems (NIPS). 2014. The project page with the source code.
E. Bernard, L. Jacob, J. Mairal and J.-P. Vert. Efficient RNA Isoform Identification and Quantification from RNA-Seq Data with Network Flows. Bioinformatics, 30(17), pages 2447–2455, 2014. source code.
Y. Chen, J. Mairal and Z. Harchaoui. Fast and Robust Archetypal Analysis for Representation Learning. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. source code. demo page.
H. O. Song, R. Girshick, S. Jegelka, J. Mairal, Z. Harchaoui and T. Darrell. On learning to localize objects with minimal supervision. International Conference on Machine Learning (ICML), 2014. source code.
A. Cherian, J. Mairal, K. Alahari and C. Schmid. Mixing Body-Part Sequences for Human Pose Estimation IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. project page, source code, dataset.
2013
2012
J. Mairal, B. Yu. Complexity Analysis of the Lasso Regularization Path. International Conference on Machine Learning (ICML), 2012. The video. source code.
J. Mairal, F. Bach and J. Ponce. Task-Driven Dictionary Learning. IEEE Pattern Analysis and Machine Intelligence (PAMI). 32(4). 2012.
F. Bach, R. Jenatton, J. Mairal and G. Obozinski. Optimization with Sparsity-Inducing Penalties. Foundations and Trends in Machine Learning, 4(1), pages 1–106, 2012. source code.
F. Bach, R. Jenatton, J. Mairal and G. Obozinski. Structured Sparsity through Convex Optimization. Statistical Science, 27(4), pages 450-468, 2012. source code.
2011
J. Mairal, R. Jenatton, G. Obozinski and F. Bach. Convex and Network Flow Optimization for Structured Sparsity. Journal of Machine Learning Research (JMLR), volume 12, pages 2681–2720, 2011. source code.
R. Jenatton, J. Mairal, G. Obozinski and F. Bach. Proximal Methods for Hierarchical Sparse Coding. Journal of Machine Learning Research (JMLR), volume 12, pages 2297-2334, 2011. source code.
J. Mairal, R. Jenatton, G. Obozinski and F. Bach. Learning Hierarchical and Topographic Dictionaries with Structured Sparsity. In proceeding of the SPIE conference on wavelets and sparsity XIV. 2011.
F. Couzinie-Devy, J. Mairal, F. Bach and J. Ponce. Dictionary Learning for Deblurring and Digital Zoom. technical report arXiv:1110.0957. 2011.
F. Bach, R. Jenatton, J. Mairal and G. Obozinski. Convex Optimization with Sparsity-Inducing Norms. In S. Sra, S. Nowozin, S. J. Wright., editors, Optimization for Machine Learning, MIT Press 2011. source code.
L. Benoit, J. Mairal, F. Bach, J. Ponce, Sparse Image Representation with Epitomes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
2010
J. Mairal. Sparse Coding for Machine Learning, Image Processing and Computer Vision. PhD thesis. Ecole Normale Superieure de Cachan, 2010.
J. Mairal, R. Jenatton, G. Obozinski and F. Bach. Network Flow Algorithms for Structured Sparsity. Adv. Neural Information Processing Systems (NIPS), 2010. source code.
R. Jenatton, J. Mairal, G. Obozinski and F. Bach. Proximal Methods for Sparse Hierarchical Dictionary Learning. International Conference on Machine Learning (ICML), 2010. source code.
J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online Learning for Matrix Factorization and Sparse Coding. Journal of Machine Learning Research (JMLR), volume 11, pages 19-60, 2010. source code.
J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S Huang and S. Yan. Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE, 98(6), pages 1031–1044, 2010.
2009
2008
J. Mairal, F. Bach, J. Ponce, G. Sapiro and A. Zisserman. Supervised Dictionary Learning. Advances Neural Information Processing Systems (NIPS), 2008.
J. Mairal, G. Sapiro and M. Elad. Learning multiscale sparse representations for image and video restoration. SIAM Multiscale Modeling and Simulation. 7(1), pages 214-241, 2008. source code (not maintained).
J. Mairal, M. Elad and G. Sapiro. Sparse representation for color image restoration. IEEE Transactions on Image Processing, 17(1), pages 53-69, 2008. The source code (not maintained).
J. Mairal, M. Leordeanu, F. Bach, M. Hebert and J. Ponce. Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation. European Conference on Computer Vision (ECCV), 2008.
J. Mairal, F. Bach, J. Ponce, G. Sapiro and A. Zisserman. Discriminative Learned Dictionaries for Local Image Analysis. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
J. Mairal, M. Elad, and G. Sapiro. Sparse Learned Representations for Image Restoration. 4th World conference of the IASC (International Association for Statistical Computing), invited paper, 2008. The source code (not maintained).
F. Bach, J. Mairal, J. Ponce, Convex Sparse Matrix Factorizations. Technical report HAL-00345747, 2008.
2007
2006
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