I am a PhD candidate in the School of Computer Science at Tel Aviv University, fortunate to be advised by Nadav Cohen.
My research interests broadly include the theoretical foundations and applications of modern machine learning. I aim to develop theories that shed light on how neural networks work, as well as bring forth principled methods for improving their efficiency, reliability, and performance.
During the summer of 2023 I interned at the Apple Machine Learning Research group. My research is generously supported by the Apple Scholars in AI/ML and the Tel Aviv University Center for AI & Data Science PhD fellowships.
Email: noamrazin (at) mail.tau.ac.il
May 24: Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States accepted to ICML 2024.
Jan 24: Two papers accepted to ICLR 2024: one identifying a vanishing gradients problem when using reinforcement learning to finetune language models and another analyzing length generalization of Transformers.
Sep 23: Two papers accepted to NeurIPS 2023: one on the ability of graph neural networks to model interactions and another on what makes data suitable for locally connected neural networks.
Sep 23: Interned at Apple Machine Learning Research.
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.
* denotes equal contribution
Analyses of Policy Gradient for Language Model Finetuning and Optimal Control
MPI MiS + UCLA Math Machine Learning Seminar, March 2024
Video
Slides
Two Analyses of Modern Deep Learning: Graph Neural Networks and Language Model Finetuning
Princeton Alg-ML Seminar, December 2023
Slides
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