Samy Jelassi

Ph.D. Student


326 Sherrerd Hall,
ORFE Department
Princeton University
sjelassi[at]princeton[dot]edu


I am a third-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 research interests are in machine learning and algorithms. In particular, I am interested in deep learning, game theory and non-convex optimization.

Publications & Preprints

Auction Learning as a two-player game
Jad Rahme, Samy Jelassi and S. Matthew Weinberg
Preprint 2020
[PDF]

A Permutation-Equivariant Neural Network Architecture For Auction Design
Jad Rahme, Samy Jelassi, Joan Bruna and S. Matthew Weinberg
Preprint 2020
[PDF]

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

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
[PDF]

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
[PDF]

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

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

Talks

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.

Teaching

I am/was teaching assistant for the following courses:

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.