Mathieu Dagréou

Mathieu Dagréou

Ph.D. student in Machine Learning

Inria Saclay - Mind team

Biography

I am a Ph.D. student at Inria in the Mind team working under the supervision of Pierre Ablin , Thomas Moreau and Samuel Vaiter . I am currently working on bilevel optimization for Machine Learning.

Interests
  • Optimization
  • Machine Learning
Education
  • MSc in Mathematics, Vision and Machine Learning (MVA), 2021

    École Normale Supérieure Paris-Saclay

  • Engineering Diploma, 2020

    École Centrale de Nantes

Papers

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(2024). A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. In AISTATS.

PDF Cite arXiv PMLR

(2023). Borne inférieure de compléxité et algorithme quasi-optimal pour la minimisation de risque empirique bi-niveaux. XXIXème Colloque Francophone de Traitement du Signal et des Images GRETSI.

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(2022). A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. In NeurIPS (Selected for an oral - Top 2% among 10,411 submissions).

PDF Cite Code Poster Slides NeurIPS arXiv

(2022). Benchopt: Reproducible, efficient and collaborative optimization benchmarks. In Neurips.

PDF Cite arXiv Benchopt

(2022). Algorithmes stochastiques et réduction de variance grâce à un nouveau cadre pour l’optimisation bi-niveaux. In XXVIIIème Colloque Francophone de Traitement du Signal et des Images GRETSI.

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Teaching

I am Teacher’s Assistant for the following course

Talks & events

  • 2024-05-02 Poster Session at AISTATS (Valencia): A lower bound a near-optimal algorithm for bilevel empirical risk minimization
  • 2023-08-31 Poster Session at GRETSI (Grenoble): A lower bound a near-optimal algorithm for bilevel empirical risk minimization
  • 2023-06-26 Poster Session at the workshop Optimization and machine learning (Toulouse): A lower bound a near-optimal algorithm for bilevel empirical risk minimization
  • 2023-02-09: Talk at Center of Data Science (ENS): A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
  • 2022-12-01: Poster session at NeurIPS (New-Orleans): A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
  • 2022-10-12: Poster session at GDR MOA days (Nice): A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
  • 2022-09-07: Poster session at GRETSI (Nancy): A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
  • 2022-06-21: Poster session at Curves and Surfaces (Arcachon): A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
  • 2022-04-05: Talk at the Parietal seminar: A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
  • 2022-03-15: Talk at Proba-Stat seminar (LJAD Nice): A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
  • 2022-03-03: Talk at the Miles team seminar (LAMSADE): A framework for bilevel optimization that enables stochastic and global variance reduction algorithms