Ohad Shamir

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 140

Top Venues

CoRR

COLT

ICML

NeurIPS

Publications

Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro
Depth Separation in Norm-Bounded Infinite-Width Neural Networks. COLT (2024)
Guy Kornowski, Ohad Shamir
Open Problem: Anytime Convergence Rate of Gradient Descent. CoRR (2024)
Guy Kornowski, Ohad Shamir
Open Problem: Anytime Convergence Rate of Gradient Descent. COLT (2024)
Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro
Depth Separation in Norm-Bounded Infinite-Width Neural Networks. CoRR (2024)
Guy Kornowski, Gilad Yehudai, Ohad Shamir
From Tempered to Benign Overfitting in ReLU Neural Networks. CoRR (2023)
Ohad Shamir
The Implicit Bias of Benign Overfitting. J. Mach. Learn. Res. 24 (2023)
Nadav Timor, Gal Vardi, Ohad Shamir
Implicit Regularization Towards Rank Minimization in ReLU Networks. ALT (2023)
Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis
Deterministic Nonsmooth Nonconvex Optimization. CoRR (2023)
Daniel Barzilai, Ohad Shamir
Generalization in Kernel Regression Under Realistic Assumptions. CoRR (2023)
Guy Kornowski, Ohad Shamir
An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization. CoRR (2023)
Nikita Kornilov, Ohad Shamir, Aleksandr V. Lobanov, Darina Dvinskikh, Alexander V. Gasnikov, Innokentiy Shibaev, Eduard Gorbunov, Samuel Horváth
Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance. NeurIPS (2023)
Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis
Deterministic Nonsmooth Nonconvex Optimization. COLT (2023)
Roey Magen, Ohad Shamir
Initialization-Dependent Sample Complexity of Linear Predictors and Neural Networks. CoRR (2023)
Roey Magen, Ohad Shamir
Initialization-Dependent Sample Complexity of Linear Predictors and Neural Networks. NeurIPS (2023)
Guy Kornowski, Gilad Yehudai, Ohad Shamir
From Tempered to Benign Overfitting in ReLU Neural Networks. NeurIPS (2023)
Gal Vardi, Gilad Yehudai, Ohad Shamir
On the Optimal Memorization Power of ReLU Neural Networks. ICLR (2022)
Gal Vardi, Gilad Yehudai, Ohad Shamir
Gradient Methods Provably Converge to Non-Robust Networks. NeurIPS (2022)
Guy Kornowski, Ohad Shamir
On the Complexity of Finding Small Subgradients in Nonsmooth Optimization. CoRR (2022)
Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract). IJCAI (2022)
Gal Vardi, Ohad Shamir, Nati Srebro
On Margin Maximization in Linear and ReLU Networks. NeurIPS (2022)
Ohad Shamir
Elephant in the Room: Non-Smooth Non-Convex Optimization. ISAIM (2022)
Ohad Shamir
The Implicit Bias of Benign Overfitting. CoRR (2022)
Gal Vardi, Gilad Yehudai, Ohad Shamir
Gradient Methods Provably Converge to Non-Robust Networks. CoRR (2022)
Gal Vardi, Ohad Shamir, Nathan Srebro
The Sample Complexity of One-Hidden-Layer Neural Networks. CoRR (2022)
Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, Michal Irani
Reconstructing Training Data from Trained Neural Networks. CoRR (2022)
Gal Vardi, Gilad Yehudai, Ohad Shamir
Width is Less Important than Depth in ReLU Neural Networks. CoRR (2022)
Gal Vardi, Gilad Yehudai, Ohad Shamir
Width is Less Important than Depth in ReLU Neural Networks. COLT (2022)
Ohad Shamir
The Implicit Bias of Benign Overfitting. COLT (2022)
Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, Michal Irani
Reconstructing Training Data From Trained Neural Networks. NeurIPS (2022)
Nadav Timor, Gal Vardi, Ohad Shamir
Implicit Regularization Towards Rank Minimization in ReLU Networks. CoRR (2022)
Gal Vardi, Ohad Shamir, Nati Srebro
The Sample Complexity of One-Hidden-Layer Neural Networks. NeurIPS (2022)
Guy Kornowski, Ohad Shamir
Oracle Complexity in Nonsmooth Nonconvex Optimization. J. Mach. Learn. Res. 23 (2022)
Gal Vardi, Daniel Reichman, Toniann Pitassi, Ohad Shamir
Size and Depth Separation in Approximating Benign Functions with Neural Networks. COLT (2021)
Itay Safran, Ohad Shamir
Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems. NeurIPS (2021)
Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. CoRR (2021)
Ohad Shamir
Gradient Methods Never Overfit On Separable Data. J. Mach. Learn. Res. 22 (2021)
Guy Kornowski, Ohad Shamir
Oracle Complexity in Nonsmooth Nonconvex Optimization. CoRR (2021)
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks. COLT (2021)
Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth
A Stochastic Newton Algorithm for Distributed Convex Optimization. NeurIPS (2021)
Gal Vardi, Gilad Yehudai, Ohad Shamir
Learning a Single Neuron with Bias Using Gradient Descent. CoRR (2021)
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks. CoRR (2021)
Gal Vardi, Ohad Shamir
Implicit Regularization in ReLU Networks with the Square Loss. COLT (2021)
Gal Vardi, Daniel Reichman, Toniann Pitassi, Ohad Shamir
Size and Depth Separation in Approximating Natural Functions with Neural Networks. CoRR (2021)
Gal Vardi, Gilad Yehudai, Ohad Shamir
Learning a Single Neuron with Bias Using Gradient Descent. NeurIPS (2021)
Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. COLT (2021)
Itay M. Safran, Gilad Yehudai, Ohad Shamir
The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks. COLT (2021)
Guy Kornowski, Ohad Shamir
Oracle Complexity in Nonsmooth Nonconvex Optimization. NeurIPS (2021)
Gal Vardi, Gilad Yehudai, Ohad Shamir
On the Optimal Memorization Power of ReLU Neural Networks. CoRR (2021)
Gal Vardi, Ohad Shamir, Nathan Srebro
On Margin Maximization in Linear and ReLU Networks. CoRR (2021)
Itay Safran, Ohad Shamir
Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems. CoRR (2021)
Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth
A Stochastic Newton Algorithm for Distributed Convex Optimization. CoRR (2021)
Liran Szlak, Ohad Shamir
Convergence Results For Q-Learning With Experience Replay. CoRR (2021)
Liran Szlak, Ohad Shamir
Replay For Safety. CoRR (2021)
Gilad Yehudai, Ohad Shamir
Learning a Single Neuron with Gradient Methods. COLT (2020)
Yoel Drori, Ohad Shamir
The Complexity of Finding Stationary Points with Stochastic Gradient Descent. ICML (2020)
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir
Proving the Lottery Ticket Hypothesis: Pruning is All You Need. CoRR (2020)
Gal Vardi, Ohad Shamir
Neural Networks with Small Weights and Depth-Separation Barriers. Electron. Colloquium Comput. Complex. 27 (2020)
Ohad Shamir
Can We Find Near-Approximately-Stationary Points of Nonsmooth Nonconvex Functions? CoRR (2020)
Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
Is Local SGD Better than Minibatch SGD? ICML (2020)
Itay Safran, Gilad Yehudai, Ohad Shamir
The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks. CoRR (2020)
Ohad Shamir
Gradient Methods Never Overfit On Separable Data. CoRR (2020)
Gilad Yehudai, Ohad Shamir
Learning a Single Neuron with Gradient Methods. CoRR (2020)
Gal Vardi, Ohad Shamir
Neural Networks with Small Weights and Depth-Separation Barriers. NeurIPS (2020)
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir
Proving the Lottery Ticket Hypothesis: Pruning is All You Need. ICML (2020)
Yossi Arjevani, Ohad Shamir, Nathan Srebro
A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates. ALT (2020)
Gal Vardi, Ohad Shamir
Neural Networks with Small Weights and Depth-Separation Barriers. CoRR (2020)
Itay Safran, Ohad Shamir
How Good is SGD with Random Shuffling? COLT (2020)
Gal Vardi, Ohad Shamir
Implicit Regularization in ReLU Networks with the Square Loss. CoRR (2020)
Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
Is Local SGD Better than Minibatch SGD? CoRR (2020)
Guy Kornowski, Ohad Shamir
High-Order Oracle Complexity of Smooth and Strongly Convex Optimization. CoRR (2020)
Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. COLT (2019)
Gilad Yehudai, Ohad Shamir
On the Power and Limitations of Random Features for Understanding Neural Networks. NeurIPS (2019)
Itay Safran, Ronen Eldan, Ohad Shamir
Depth Separations in Neural Networks: What is Actually Being Separated? CoRR (2019)
Yuval Dagan, Gil Kur, Ohad Shamir
Space lower bounds for linear prediction. CoRR (2019)
Yoel Drori, Ohad Shamir
The Complexity of Finding Stationary Points with Stochastic Gradient Descent. CoRR (2019)
Yuval Dagan, Gil Kur, Ohad Shamir
Space lower bounds for linear prediction in the streaming model. COLT (2019)
Ohad Shamir
Exponential Convergence Time of Gradient Descent for One-Dimensional Deep Linear Neural Networks. COLT (2019)
Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. CoRR (2019)
Gilad Yehudai, Ohad Shamir
On the Power and Limitations of Random Features for Understanding Neural Networks. CoRR (2019)
Yossi Arjevani, Ohad Shamir, Ron Shiff
Oracle complexity of second-order methods for smooth convex optimization. Math. Program. 178 (1-2) (2019)
Itay Safran, Ronen Eldan, Ohad Shamir
Depth Separations in Neural Networks: What is Actually Being Separated? COLT (2019)
Itay Safran, Ohad Shamir
How Good is SGD with Random Shuffling? CoRR (2019)
Noah Golowich, Alexander Rakhlin, Ohad Shamir
Size-Independent Sample Complexity of Neural Networks. COLT (2018)
Ohad Shamir
Are ResNets Provably Better than Linear Predictors? CoRR (2018)
Yossi Arjevani, Ohad Shamir, Nathan Srebro
A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates. CoRR (2018)
Yuval Dagan, Ohad Shamir
Detecting Correlations with Little Memory and Communication. COLT (2018)
Murat A. Erdogdu, Lester Mackey, Ohad Shamir
Global Non-convex Optimization with Discretized Diffusions. CoRR (2018)
Ohad Shamir
Exponential Convergence Time of Gradient Descent for One-Dimensional Deep Linear Neural Networks. CoRR (2018)
Nicolò Cesa-Bianchi, Ohad Shamir
Bandit Regret Scaling with the Effective Loss Range. ALT (2018)
Ohad Shamir
Distribution-Specific Hardness of Learning Neural Networks. J. Mach. Learn. Res. 19 (2018)
Ohad Shamir
Are ResNets Provably Better than Linear Predictors? NeurIPS (2018)
Yuval Dagan, Ohad Shamir
Detecting Correlations with Little Memory and Communication. CoRR (2018)
Itay Safran, Ohad Shamir
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks. ICML (2018)
Murat A. Erdogdu, Lester Mackey, Ohad Shamir
Global Non-convex Optimization with Discretized Diffusions. NeurIPS (2018)
Dan Garber, Ohad Shamir, Nathan Srebro
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. CoRR (2017)
Noah Golowich, Alexander Rakhlin, Ohad Shamir
Size-Independent Sample Complexity of Neural Networks. CoRR (2017)
Ohad Shamir, Liran Szlak
Online Learning with Local Permutations and Delayed Feedback. CoRR (2017)
Ohad Shamir, Liran Szlak
Online Learning with Local Permutations and Delayed Feedback. ICML (2017)
Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah
Failures of Deep Learning. CoRR (2017)
Itay Safran, Ohad Shamir
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks. CoRR (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Ohad Shamir