Publications

Preprints

• D. Wynen, C. Schmid and J. Mairal. Learning Latent Representations of Style with Archetypal Style Analysis.
  submitted. 2019.
  
• N. Dvornik, C. Schmid and J. Mairal. Diversity with Cooperation: Ensemble Methods for Few-Shot Classification.
  preprint arXiv:1903.11341. 2019.
• A. Kulunchakov and J. Mairal. Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise.
  preprint arXiv:1901.08788. 2019.
• A. Mensch, J. Mairal, B. Thirion and G. Varoquaux. Extracting Universal Representations of Cognition across Brain-Imaging Studies.
  preprint arXiv:1809.06035. 2019.
• M. Dvornik, J. Mairal and C. Schmid. On the Importance of Visual Context for Data Augmentation in Scene Understanding.
  preprint arXiv:1809.02492. 2019.
• S. Arlot, A. Celisse, and Z. Harchaoui. A kernel multiple change-point algorithm via model selection.
  preprint arXiv:1202.3878. 2019.
• C. Paquette, H. Lin, D. Drusvyatskiy, J. Mairal, Z. Harchaoui. Catalyst Acceleration for Gradient-Based Non-Convex Optimization.
  preprint arXiv:1703.10993. 2018.
• N. He, Z. Harchaoui, Y. Wang and L. Song. Fast and Simple Optimization for Poisson Likelihood Models.
  preprint arXiv:1608.01264. 2016.

International Journals and Conferences

• A. Kulunchakov and J. Mairal. Estimate Sequences for Variance-Reduced Stochastic Composite Optimization.
  to appear at International Conference on Machine Learning (ICML). 2019.
• A. Bietti, G. Mialon, D. Chen, and J. Mairal. A Kernel Perspective for Regularizing Deep Neural Networks.
  to appear at International Conference on Machine Learning (ICML). 2019.
• H. Lin, J. Mairal and Z. Harchaoui. An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration.
  to appear in SIAM Journal on Optimization. 2019.
• D. Chen, L. Jacob, and J. Mairal. Biological Sequence Modeling with Convolutional Kernel Networks.
  Bioinformatics (also RECOMB). 2019.
  source code.
• A. Bietti and J. Mairal. Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations.
  Journal of Machine Learing Research (JMLR). 20(25), 2019.
• G. Durif, L. Modolo, J. Mold, S. Lambert-Lacroix and F. Picard. Probabilistic Count Matrix Factorization for Single Cell Expression Data Analysis.
  Bioinformatics (also RECOMB). 2019.
• M. Jaillard, L. Lima, M. Tournoud, P. Mahe, A. van Belkum, V. Lacroix, and L. Jacob. A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events.
  PLoS Genetics. 2018.
• D. Wynen, C. Schmid and J. Mairal. Unsupervised Learning of Artistic Styles with Archetypal Style Analysis.
  Adv. Neural Information Processing Systems (NeurIPS/NIPS). 2018.
  project page.
• L. Jacob and T. Speed. The healthy ageing gene expression signature for Alzheimer's disease diagnosis: a random sampling perspective.
  Genome Biology. 19(97), 2018
• L. Jacob, F. Combes and T. Burger. PEPA test: fast and powerful differential analysis from relative quantitative proteomics data using shared peptides.
  Biostatistics. 2018
• 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.
• 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). 35(9), pages 2318 -- 2326, 2018.
  source code.
• M. Dvornik, J. Mairal and C. Schmid. Modeling Visual Context is Key to Augmenting Object Detection Datasets.
  European Conference on Computer Vision (ECCV). 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.
• 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.
• G. Durif, L. Modolo, J. Michaelsson, J. Mold, S. Lambert-Lacroix and F. Picard. High Dimensional Classification with combined Adaptive Sparse PLS and Logistic Regression.
  Bioinformatics. 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). 2017.
  project page + source code
• J. Mairal. End-to-end kernel learning with supervised convolutional neural networks.
  Adv. Neural Information Processing Systems (NIPS). 2016.
  source code.
• D. Ostrovsky, Z. Harchaoui, A. Juditsky and A. Nemirovsky. Structure-Blind Signal Recovery.
  Adv. Neural Information Processing Systems (NIPS). 2016.
• 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
• H. Lin, J. Mairal, and Z. Harchaoui. A Universal Catalyst for First-Order Optimization.
  Adv. Neural Information Processing Systems (NIPS). 2015
• N. He and Z. Harchaoui. Semi-proximal Mirror-Prox for Nonsmooth Composite Minimization.
  Adv. Neural Information Processing Systems (NIPS). 2015
• M. Paulin, M. Douze, Z. Harchaoui, J. Mairal, F. Perronin et C. Schmid. Local Convolutional Features with Unsupervised Training for Image Retrieval.
  International Conference on Computer Vision (ICCV) 2015.
  project page + source code
• Z Harchaoui, A Juditsky, A Nemirovski, D Ostrovsky. Adaptive Recovery of Signals by Convex Optimization.
  Conference on Learning Theory (COLT) 2015.
• 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. Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning.
  SIAM Journal on Optimization. 25(2), pages 829--855, 2015.
  source code.
• 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.
• J. Mairal, F. Bach and J. Ponce. Sparse Modeling for Image and Vision Processing.
  Foundations and Trends in Computer Graphics and Vision. 8(2-3), pages 85--283, 2014.

Other publications

• A. Mensch, J. Mairal, B. Thirion and G. Varoquaux. Subsampling Enables Fast Factorisation of Huge Matrices into Sparse Signals
  ERCIM News 108. 2017.
• J. Mairal. Algorithmes de Majorisation-Minimisation et Factorisation Structurée de Matrices.
  Gretsi. 2015.