Samy Jelassi

Ph.D. Student

326 Sherrerd Hall,
ORFE Department
Princeton University

I am a fourth-year Ph.D. student in the ORFE Department at Princeton University where I am fortunate to be advised by Prof. Yoram Singer and Prof. Joan Bruna.

Prior to that, I obtained a B.S. in Computer Science from Ecole Normale Superieure de Lyon and a M.S. in Applied Mathematics (Master MVA) from Ecole Normale Superieure Paris-Saclay. More details can be found in my CV.

My main research interests are in deep learning optimization and game optimization. I am also interested in game theory in general and its intersection with deep learning.

Publications & Preprints

Depth separation beyond radial functions
Luca Venturi, Samy Jelassi, Tristan Ozuch and Joan Bruna
Preprint 2021

Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization
Aaron Defazio and Samy Jelassi
Preprint 2021

Dual Averaging is Surprisingly Effective for Deep Learning Optimization
Samy Jelassi and Aaron Defazio
Preprint 2020

Auction Learning as a two-player game
Jad Rahme, Samy Jelassi and S. Matthew Weinberg
ICLR 2021

A Permutation-Equivariant Neural Network Architecture For Auction Design
Jad Rahme, Samy Jelassi, Joan Bruna and S. Matthew Weinberg
AAAI 2021

A mean-field analysis of two-player zero-sum games
Carles Domingo-Enrich, Samy Jelassi, Arthur Mensch, Grant M. Rotskoff and Joan Bruna
NeurIPS 2020

Extra-gradient with player sampling for provable fast convergence in n-player games
Samy Jelassi, Carles Domingo Enrich, Damien Scieur, Arthur Mensch and Joan Bruna
ICML 2020

Towards closing the gap between the theory and practice of SVRG
Othmane Sebbouh, Nidham Gazagnadou, Samy Jelassi, Francis Bach and Robert M. Gower
NeurIPS 2019

Global convergence of neuron birth-death dynamics
Grant M. Rotskoff, Samy Jelassi, Joan Bruna and Eric Vanden-Eijnden
ICML 2019

Smoothed analysis of the low-rank approach for smooth semidefinite programs
Thomas Pumir*, Samy Jelassi* and Nicolas Boumal
*Equal contribution
NeurIPS 2018 (Oral presentation)


02/2021: Tutorial on implicit bias in machine learning problems. Deep Learning theory seminar at Princeton University, USA.

10/2019: Smoothed analysis for some machine learning problems. Google Brain, Montreal, Canada.

02/2019: Global Convergence of the neuron birth-death dynamics. Math and Deep Learning seminar at New York University, USA.

12/2018: Smoothed analysis of the low-rank approach for smooth semidefinite programs. Oral presentation at the NeurIPS conference, Montreal, Canada.

11/2018: Handling non-convexity in low rank approaches for semidefinite programming. MIC seminar at New York University, USA.


I am/was teaching assistant for the following courses:

Spring 2021: ORF 350: Analysis of Big Data.
Fall 2020: ORF 409: Introduction to Monte Carlo Simulation.
Fall 2019: ELE 435/535: Machine Learning and Pattern Recognition.
Summer 2019: Deep Learning theory at MSRI summer school.
Spring 2019: ORF 350: Analysis of Big Data.
Fall 2018: ELE 435/535: Machine Learning and Pattern Recognition.