Biography

I am a PhD student since September 2021 under the supervision of Laurent Massoulié, working on the theory and algorithms of Machine Learning. I am particularly interested in optimization (in all its form: distributed, decentralized, stochastic,…), topics related to Federated Learning, gossip algorithms and high-dimensional statistics.

Interests
  • Optimization
  • Statistics
  • Surfing
  • Hiking
Education
  • Master Program in Probability and Statistics, 2020

    Université d'Orsay/Paris-Saclay

  • Mathematics and Physics studies

    Ecole Normale Supérieure de Paris

Publications

(2023). Implicit Regularisation, Large Stepsizes and Edge of Stability for (S)GD over Diagonal Linear Networks. preprint.

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(2022). Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays. Neural Information Processing Systems (NeurIPS), 2022.

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(2022). Muffliato Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging. Neural Information Processing Systems (NeurIPS), 2021.

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(2022). On Sample Optimality in Personalized Federated and Collaborative Learning. Neural Information Processing Systems (NeurIPS), 2022.

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(2021). A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip. NeurIPS2021 Outstanding Paper Award.

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(2021). Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction. ICML2021.

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(2021). Asynchrony and Acceleration in Gossip Algorithms.

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Conferences and Workshops

- Prairie Workshop, Paris, October 10, 2021.

- Workshop On Future Synergies for Stochastic and Learning Algorithms, CIRM, Luminy, Sept. 27-Oct. 1, 2021.

- Conference on Learning Theory (COLT), August 15-19, 2021. (talk,slides,poster)

- International Conference on Machine Learning (ICML), July 18-24, 2021. (spotlight presentation)

Teaching

- Mathematics of Deep-Learning, ENS Paris, Fall 2021. Master course, TDs with Kevin Scaman. Course Website

- Network Algorithms, ENS Paris, Fall 2021. Master course, TDs with Ana Busic.

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