Selected research

  • A. Mensch, J. Mairal, B. Thirion, and G. Varoquaux, “Extracting representations of cognition across neuroimaging studies improves brain decoding,” PLOS Computational Biology, vol. 17, no. 5, pp. 1–20, May 2021. PDF
  • C. Apra, C. Caucheteux, A. Mensch, and others, “Predictive usefulness of PCR testing in different patterns of Covid-19 symptomatology - Analysis of a French cohort of 12,810 outpatients,” Scientific Reports, 2021. PDF
  • C. Domingo-Enrich, S. Jelassi, A. Mensch, G. Rotskoff, and J. Bruna, “A mean-field analysis of two-player zero-sum games,” in Advances in Neural Information Processing Systems, Dec. 2020. PDF
  • A. Mensch and G. Peyré, “Online Sinkhorn: Optimal Transport distances from sample streams,” in Advances in Neural Information Processing Systems, Dec. 2020. PDF
  • S. Jelassi, C. Domingo-Enrich, D. Scieur, A. Mensch, and J. Bruna, “Extra-gradient with player sampling for faster convergence in n-player games,” in International Conference on Machine Learning, Jun. 2020. PDF
  • K. Dadi, G. Varoquaux, A. Machlouzarides-Shalit, K. J. Gorgolewski, D. Wassermann, B. Thirion, and A. Mensch, “Fine-grain atlases of functional modes for fMRI analysis,” NeuroImage, vol. 221, p. 117126, Jan. 2020. PDF
  • A. Mensch, M. Blondel, and G. Peyré, “Geometric losses for distributional learning,” in Proceedings of the International Conference in Machine Learning, Feb. 2019. PDF
  • A. Mensch, J. Mairal, B. Thirion, and G. Varoquaux, “Extracting universal representations of cognition across brain-imaging studies,” Sep. 2018. PDF
  • A. Mensch and M. Blondel, “Differentiable dynamic programming for structured prediction and attention,” in Proceedings of the International Conference in Machine Learning, Jul. 2018, pp. 3462–3471. PDF
  • A. Mensch, J. Mairal, B. Thirion, and G. Varoquaux, “Stochastic subsampling for factorizing huge matrices,” IEEE Transactions on Signal Processing, vol. 66, no. 1, pp. 113–128, Jan. 2018. PDF
  • A. Mensch, “Learning representations from functional MRI data,” PhD thesis, Université Paris-Saclay, 2018. PDF
  • A. Mensch, J. Mairal, D. Bzdok, B. Thirion, and G. Varoquaux, “Learning neural representations of human cognition across many fMRI studies,” in Advances in Neural Information Processing Systems, Dec. 2017, pp. 5885–5895. PDF
  • E. Dohmatob, A. Mensch, G. Varoquaux, and B. Thirion, “Learning brain regions via large-scale online structured sparse dictionary learning,” in Advances in Neural Information Processing Systems, Dec. 2016, pp. 4610–4618. PDF
  • A. Mensch, J. Mairal, B. Thirion, and G. Varoquaux, “Dictionary learning for massive matrix factorization,” in Proceedings of the International Conference on Machine Learning, Jun. 2016, pp. 1737–1746. PDF
  • A. Mensch, G. Varoquaux, and B. Thirion, “Compressed online dictionary learning for fast resting-state fMRI decomposition,” in IEEE International Symposium on Biomedical Imaging, Apr. 2016. PDF
  • A. Mensch, E. Piuze, L. Lehnert, A. J. Bakermans, J. Sporring, G. J. Strijkers, and K. Siddiqi, “Connection forms for beating the heart,” in MICCAI Workshop on Statistical Atlases and Computational Models of the Heart, Sep. 2014.

Oral presentations

  • Geometric losses for distributional learning Paper
    • June 2019: ICML (Long Beach, USA) Slides
  • Differentiable Dynamic Programming for Structured Prediction and Attention Paper
    • April 2019: Courant Institute, NYU (New York, Paris) Slides, hosted by Joan Bruna
    • January 2019: Google (Zürich, Switzerland) Slides
    • July 2018: ICML (Stockholm, Sweden) Slides
    • April 2018: Facebook Artificial Intelligence Research (Paris, France) Slides
    • March 2018: Deep Learning Meetup (Paris, France) Slides
  • Learning representations from functional MRI Data Thesis
    • November 2018: Laplace Seminar, ENS, Paris, hosted by Nicolas Keriven Slides
    • September 2018: PhD defense Slides
  • Stochastic Subsampling for Factorizing Huge Matrices Paper
    • July 2018: ISMP 2018 (Bordeaux, France), invited by Robert Gower Slides
    • April 2018: Aix-Marseille Université, invited by Caroline Chaux-Moulin Slides
    • January 2018: ENS Ulm (Paris, France), invited by Florent Krzakala Slides
  • Learning Neural Representations of Human Cognition across Many fMRI Studies
  • Massive Matrix Factorization for Resting-State fMRI Decomposition
  • Massive Matrix Factorization : Application to collaborative filtering

Posters

  • ICML 2019: Geometric losses for distributional learning
  • ICML 2018: Differentiable Dynamic Programming for Structured Prediction and Attention
  • OPT 2017, SMAI-MODE 2018: Stochastic Subsampling for Factorizing Huge Matrices
  • ICML 2016: Dictionary Learning for Massive Matrix Factorization
  • ISBI 2016: Compressed Online Dictionary Learning for Fast fMRI Decomposition
  • OHBM 2016: Compressed Online Dictionary Learning for Fast fMRI Decomposition

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