Itay Safran - RankMe

29 papers A* 11Journal 18

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Depth Hierarchy for Computing the Maximum in ReLU Networks via Extremal Graph Theory. CoRR Itay Safran
2026	J	`jnl`	On the Rate of Convergence of GD in Non-linear Neural Networks: An Adversarial Robustness Perspective. CoRR Guy Smorodinsky, Sveta Gimpleson, Itay Safran
2026	J	`jnl`	The Median is Easier than it Looks: Approximation with a Constant-Depth, Linear-Width ReLU Network. CoRR Abhigyan Dutta, Itay Safran, Paul Valiant
2026	J	`jnl`	To Grok Grokking: Provable Grokking in Ridge Regression. CoRR Mingyue Xu, Gal Vardi, Itay Safran
2025	A*	`conf`	Depth Separations in Neural Networks: Separating the Dimension from the Accuracy. COLT Itay Safran, Daniel Reichman, Paul Valiant
2025	J	`jnl`	No Prior, No Leakage: Revisiting Reconstruction Attacks in Trained Neural Networks. CoRR Yehonathan Refael, Guy Smorodinsky, Ofir Lindenbaum, Itay Safran
2024	J	`jnl`	Depth Separations in Neural Networks: Separating the Dimension from the Accuracy. CoRR Itay Safran, Daniel Reichman, Paul Valiant
2024	A*	`conf`	How Many Neurons Does it Take to Approximate the Maximum? SODA Itay Safran, Daniel Reichman, Paul Valiant
2024	J	`jnl`	Provable Privacy Attacks on Trained Shallow Neural Networks. CoRR Guy Smorodinsky, Gal Vardi, Itay Safran
2023	J	`jnl`	How Many Neurons Does it Take to Approximate the Maximum? CoRR Itay Safran, Daniel Reichman, Paul Valiant
2022	A*	`conf`	On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias. NeurIPS Itay Safran, Gal Vardi, Jason D. Lee
2022	J	`jnl`	On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias. CoRR Itay Safran, Gal Vardi, Jason D. Lee
2022	A*	`conf`	Optimization-Based Separations for Neural Networks. COLT Itay Safran, Jason D. Lee
2021	J	`jnl`	Optimization-Based Separations for Neural Networks. CoRR Itay Safran, Jason D. Lee
2021	A*	`conf`	Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems. NeurIPS Itay Safran, Ohad Shamir
2021	J	`jnl`	Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems. CoRR Itay Safran, Ohad Shamir
2021	A*	`conf`	The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks. COLT Itay Safran, Gilad Yehudai, Ohad Shamir
2020	A*	`conf`	How Good is SGD with Random Shuffling? COLT Itay Safran, Ohad Shamir
2020	J	`jnl`	The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks. CoRR Itay Safran, Gilad Yehudai, Ohad Shamir
2019	J	`jnl`	A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance. CoRR Adi Shamir, Itay Safran, Eyal Ronen, Orr Dunkelman
2019	A*	`conf`	Depth Separations in Neural Networks: What is Actually Being Separated? COLT Itay Safran, Ronen Eldan, Ohad Shamir
2019	J	`jnl`	Depth Separations in Neural Networks: What is Actually Being Separated? CoRR Itay Safran, Ronen Eldan, Ohad Shamir
2019	J	`jnl`	How Good is SGD with Random Shuffling? CoRR Itay Safran, Ohad Shamir
2018	A*	`conf`	Spurious Local Minima are Common in Two-Layer ReLU Neural Networks. ICML Itay Safran, Ohad Shamir
2017	A*	`conf`	Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks. ICML Itay Safran, Ohad Shamir
2017	J	`jnl`	Spurious Local Minima are Common in Two-Layer ReLU Neural Networks. CoRR Itay Safran, Ohad Shamir
2016	J	`jnl`	Depth Separation in ReLU Networks for Approximating Smooth Non-Linear Functions. CoRR Itay Safran, Ohad Shamir
2016	A*	`conf`	On the Quality of the Initial Basin in Overspecified Neural Networks. ICML Itay Safran, Ohad Shamir
2015	J	`jnl`	On the Quality of the Initial Basin in Overspecified Neural Networks. CoRR Itay Safran, Ohad Shamir