Nov 22: New paper quantifies the ability of graph neural networks to model interactions between vertices. Our theory leads to a simple and efficient edge sparsification algorithm that outperforms alternative methods.

May 22: Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks accepted to ICML 2022.
📝 Check out this blog post for an overview.

Mar 22: Honored to receive the 2022 Apple Scholars in AI/ML PhD fellowship.

Oct 21: Honored to receive the Tel Aviv University Center for AI & Data Science excellence fellowship.

May 21: Implicit Regularization in Tensor Factorization accepted to ICML 2021.
📝 Check out this blog post for an overview.

Sep 20: Implicit Regularization in Deep Learning May Not Be Explainable by Norms accepted to NeurIPS 2020.
📝 Check out this blog post for an overview.

On the Ability of Graph Neural Networks to Model Interactions Between Vertices
Learning on Graphs and Geometry Reading Group, January 2023
Video Slides

Generalization in Deep Learning Through the Lens of Implicit Rank Lowering
ICTP Youth in High-Dimensions: Recent Progress in Machine Learning, High-Dimensional Statistics and Inference, June 2022
Video Slides

Generalization in Deep Learning Through the Lens of Implicit Rank Lowering
MPI MiS + UCLA Math Machine Learning Seminar, May 2022
Slides

Implicit Regularization in Tensor Factorization
The Hebrew University Machine Learning Club, Jerusalem, Israel, June 2021
Video Slides

Implicit Regularization in Deep Learning May Not Be Explainable by Norms
Tel Aviv University Machine Learning Seminar, Tel Aviv, Israel, May 2020
Slides

Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Networks, Off the Convex Path, July 22

Implicit Regularization in Tensor Factorization: Can Tensor Rank Shed Light on Generalization in Deep Learning?, Off the Convex Path, July 21

Can Implicit Regularization in Deep Learning Be Explained by Norms? (by Nadav Cohen), Off the Convex Path, November 20