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Related publications
2022
G. Beugnot, J. Mairal, and A. Rudi. On the Benefits of Large Learning Rates for Kernel Methods. preprint arXiv:2202.13733. 2022.
E. Fini, V. G. Turrisi da Costa, X. Alameda-Pineda, E. Ricci, K. Alahari and J. Mairal. Self-Supervised Models are Continual Learners. to appear at the 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. Jégou, 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.
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.
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
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