2026 Ohad Shamir: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	62	2963	2877	51	48	155	11952

Overview

Ohad Shamir is affiliated with the Weizmann Institute of Science in Israel, focusing primarily on research in computer science. Their scholarly work spans main fields and subfields, emphasizing areas linked to artificial intelligence and computational methodologies.

Their research contributions cover the following main fields of study:

Computer Science

Within computer science, their subfields of study include:

Artificial Intelligence
Computational Mechanics
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering
Computer Networks and Communications

Shamir's work encompasses a range of topics, with significant focus on optimization and machine learning methods:

Stochastic Gradient Optimization Techniques
Machine Learning and Algorithms
Neural Networks and Applications
Sparse and Compressive Sensing Techniques
Machine Learning and ELM (Extreme Learning Machines)
Adversarial Robustness in Machine Learning
Privacy-Preserving Technologies in Data

The scientist has contributed numerous papers to various venues, frequently publishing in arXiv (Cornell University). Other venues include Constructive Approximation, the Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, The Annals of Statistics, and the International Journal of Emerging Trends in Computer Science and Information Technology.

Among their recent papers are:

"Proving the Lottery Ticket Hypothesis: Pruning is All You Need," 2020, arXiv (Cornell University)
"Is Local SGD Better than Minibatch SGD?," 2020, arXiv (Cornell University)
"Reconstructing Training Data from Trained Neural Networks," 2022, arXiv (Cornell University)
"Learning a Single Neuron with Gradient Methods," 2025, arXiv (Cornell University)
"The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication," 2021, arXiv (Cornell University)

Shamir frequently collaborates with several coauthors, including:

Gilad Yehudai
Gal Vardi
Guy Kornowski
Nathan Srebro
Itay Safran

Best Publications

Optimal distributed online prediction using mini-batches

Ofer Dekel;Ran Gilad-Bachrach;Ohad Shamir;Lin Xiao

688
Citations
The Power of Depth for Feedforward Neural Networks

Ronen Eldan;Ohad Shamir

683
Citations
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization

Alexander Rakhlin;Ohad Shamir;Karthik Sridharan

640
Citations
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes

Ohad Shamir;Tong Zhang

524
Citations
Communication-Efficient Distributed Optimization using an Approximate Newton-type Method

Ohad Shamir;Nati Srebro;Tong Zhang

487
Citations
Learnability, Stability and Uniform Convergence

Shai Shalev-Shwartz;Ohad Shamir;Nathan Srebro;Karthik Sridharan

412
Citations
Size-independent sample complexity of neural networks

Noah Golowich;Alexander Rakhlin;Ohad Shamir

370
Citations
Stochastic Convex Optimization.

Shai Shalev-Shwartz;Ohad Shamir;Nathan Srebro;Karthik Sridharan

297
Citations
On the Computational Efficiency of Training Neural Networks

Roi Livni;Shai Shalev-Shwartz;Ohad Shamir

294
Citations
Learning and generalization with the information bottleneck

Ohad Shamir;Sivan Sabato;Naftali Tishby

249
Citations
An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback

Ohad Shamir

236
Citations
Adaptively Learning the Crowd Kernel

Omer Tamuz;Ce Liu;Serge Belongie;Ohad Shamir

235
Citations
Learning to classify with missing and corrupted features

Ofer Dekel;Ohad Shamir;Lin Xiao

217
Citations
Better Mini-Batch Algorithms via Accelerated Gradient Methods

Andrew Cotter;Ohad Shamir;Nati Srebro;Karthik Sridharan

216
Citations
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks.

Itay Safran;Ohad Shamir

196
Citations
Distributed stochastic optimization and learning

Ohad Shamir;Nathan Srebro

190
Citations
Vox Populi: Collecting High-Quality Labels from a Crowd

Ofer Dekel;Ohad Shamir

171
Citations
A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate

Ohad Shamir

170
Citations
On the Complexity of Bandit and Derivative-Free Stochastic Convex Optimization

Ohad Shamir

169
Citations
Multi-player bandits: a musical chairs approach

Jonathan Rosenski;Ohad Shamir;Liran Szlak

169
Citations
Communication complexity of distributed convex learning and optimization

Yossi Arjevani;Ohad Shamir

126
Citations
Is Local SGD Better than Minibatch SGD

Blake Woodworth;Kumar Kshitij Patel;Sebastian Stich;Zhen Dai

104
Citations
Failures of Gradient-Based Deep Learning

Shai Shalev-Shwartz;Ohad Shamir;Shaked Shammah

98
Citations
Proving the Lottery Ticket Hypothesis: Pruning is All You Need

Gilad Yehudai;Eran Malach;Shai Shalev-Schwartz;Ohad Shamir

72
Citations

Frequent Co-Authors

Shai Shalev-Shwartz Hebrew University of Jerusalem

Nicolò Cesa-Bianchi University of Milan

Karthik Sridharan Cornell University

Nathan Srebro Toyota Technological Institute at Chicago

Ofer Dekel Microsoft (United States)

Naftali Tishby Hebrew University of Jerusalem

Shie Mannor Technion – Israel Institute of Technology

Alexander Rakhlin MIT

Adam Tauman Kalai Microsoft (United States)

Lin Xiao Facebook (United States)

External Links

Personal website of Ohad Shamir Google Scholar page

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