On the Quality of the Initial Basin in Overspecified Neural Networks. - Itay Safran; Ohad Shamir (ICML, 2016)

Depth Separation in ReLU Networks for Approximating Smooth Non-Linear Functions. - Itay Safran; Ohad Shamir (CoRR, 2016)

Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks. - Itay Safran; Ohad Shamir (ICML, 2017)

Spurious Local Minima are Common in Two-Layer ReLU Neural Networks. - Itay Safran; Ohad Shamir (ICML, 2018)

Depth Separations in Neural Networks: What is Actually Being Separated? - Itay Safran; Ronen Eldan; Ohad Shamir (COLT, 2019)

A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance. - Adi Shamir; Itay Safran; Eyal Ronen; Orr Dunkelman (CoRR, 2019)

How Good is SGD with Random Shuffling? - Itay Safran; Ohad Shamir (COLT, 2020)

The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks. - Itay Safran; Gilad Yehudai; Ohad Shamir (COLT, 2021)

Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems. - Itay Safran; Ohad Shamir (NeurIPS, 2021)

Optimization-Based Separations for Neural Networks. - Itay Safran; Jason D. Lee (COLT, 2022)

On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias. - Itay Safran; Gal Vardi; Jason D. Lee (NeurIPS, 2022)

How Many Neurons Does it Take to Approximate the Maximum? - Itay Safran; Daniel Reichman 001; Paul Valiant (SODA, 2024)

Provable Privacy Attacks on Trained Shallow Neural Networks. - Guy Smorodinsky; Gal Vardi; Itay Safran (CoRR, 2024)

Depth Separations in Neural Networks: Separating the Dimension from the Accuracy. - Itay Safran; Daniel Reichman 001; Paul Valiant (COLT, 2025)