Baptiste Goujaud

Baptiste Goujaud

Assistant Professor

Telecom SudParis

Biography

I am an Assistant Professor in the Citi team at Telecom SudParis, supported by a Hi! PARIS Chair.

Previously, I completed a PhD in the CMAP lab at Ecole Polytechnique under the supervision of Aymeric Dieuleveut (Ecole Polytechnique) and Adrien Taylor (INRIA), and was a postdoctoral researcher in the MIND team at INRIA Saclay, working on Optimization with Thomas Moreau (INRIA) and Pierre Ablin (Apple).

My research interests include Machine Learning, Optimization, and Statistics. In particular, I am interested in distributed learning, generalization, privacy, robustness, diffusion processes, and related topics.

Open Source & Packages

  • PEPit — Python package for Performance Estimation Problems.
  • Practical Exercises — Learning Performance Estimation repository.
Interests
  • Optimization
  • Distributed Learning
  • Robustness
  • Diffusion models
Education
  • PhD in Optimization for Machine Learning, 2024

    Ecole Polytechnique

  • Master of Science in Machine Learning and Computer Vision (MVA), 2017

    Ecole Normale Supérieure Paris-Saclay

  • Agrégation de mathématiques, ranked 18th, 2016

    Ecole Normale Supérieure Paris-Saclay

Publications

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(2024). Proving linear mode connectivity of neural networks via optimal transport. AISTATS 2024.

(2024). PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python. Mathematical Programming Computation (MPC).

(2023). Counter-examples in first-order optimization: a constructive approach. L-CSS.

(2023). Gradient descent is optimal under lower restricted secant inequality and upper error bound. NeurIPS 2023.

(2023). On Fundamental Proof Structures in First-Order Optimization. CDC 2023.