Publications (56)
Learning Kernel-Based Halfspaces with the Zero-One Loss
Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan
On the Iteration Complexity of Oblivious First-Order Optimization Algorithms
Yossi Arjevani, Ohad Shamir
Communication Complexity of Distributed Convex Learning and Optimization
Yossi Arjevani, Ohad Shamir
An Algorithm for Training Polynomial Networks
Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
Efficient Transductive Online Learning via Randomized Rounding
Nicolò Cesa-Bianchi, Ohad Shamir
Efficient Learning with Partially Observed Attributes
Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir
Detecting Correlations with Little Memory and Communication
Yuval Dagan, Ohad Shamir
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes
Ohad Shamir, Tong Zhang
Weight Sharing is Crucial to Succesful Optimization
Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah
Large-Scale Convex Minimization with a Low-Rank Constraint
Shai Shalev-Shwartz, Alon Gonen, Ohad Shamir
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation
Ohad Shamir
Graph Approximation and Clustering on a Budget
Ethan Fetaya, Ohad Shamir, Shimon Ullman
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression
Sham Kakade, Adam Tauman Kalai, Varun Kanade +1
On the Complexity of Bandit and Derivative-Free Stochastic Convex Optimization
Ohad Shamir
Communication Efficient Distributed Optimization using an Approximate Newton-type Method
Ohad Shamir, Nathan Srebro, Tong Zhang
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis
Dan Garber, Ohad Shamir, Nathan Srebro
On the Quality of the Initial Basin in Overspecified Neural Networks
Itay Safran, Ohad Shamir
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization
Alexander Rakhlin, Ohad Shamir, Karthik Sridharan
From Bandits to Experts: On the Value of Side-Observations
Shie Mannor, Ohad Shamir
On Lower and Upper Bounds for Smooth and Strongly Convex Optimization Problems
Yossi Arjevani, Shai Shalev-Shwartz, Ohad Shamir
A Variant of Azuma's Inequality for Martingales with Subgaussian Tails
Ohad Shamir
Optimal Distributed Online Prediction using Mini-Batches
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir +1
Without-Replacement Sampling for Stochastic Gradient Methods: Convergence Results and Application to Distributed Optimization
Ohad Shamir
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
Itay Safran, Ohad Shamir
Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity
Ohad Shamir
Multi-Player Bandits -- a Musical Chairs Approach
Jonathan Rosenski, Ohad Shamir, Liran Szlak
Online Learning for Time Series Prediction
Oren Anava, Elad Hazan, Shie Mannor +1
Failures of Gradient-Based Deep Learning
Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah
Relax and Localize: From Value to Algorithms
Alexander Rakhlin, Ohad Shamir, Karthik Sridharan
Learning with the Weighted Trace-norm under Arbitrary Sampling Distributions
Rina Foygel, Ruslan Salakhutdinov, Ohad Shamir +1
Using More Data to Speed-up Training Time
Shai Shalev-Shwartz, Ohad Shamir, Eran Tromer
Space lower bounds for linear prediction in the streaming model
Yuval Dagan, Gil Kur, Ohad Shamir
On the Complexity of Learning with Kernels
Nicolò Cesa-Bianchi, Yishay Mansour, Ohad Shamir
Online Learning with Switching Costs and Other Adaptive Adversaries
Nicolo Cesa-Bianchi, Ofer Dekel, Ohad Shamir
Online Learning with Local Permutations and Delayed Feedback
Ohad Shamir, Liran Szlak
The Power of Depth for Feedforward Neural Networks
Ronen Eldan, Ohad Shamir
Are ResNets Provably Better than Linear Predictors?
Ohad Shamir
Robust Distributed Online Prediction
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir +1
Accurate Profiling of Microbial Communities from Massively Parallel Sequencing using Convex Optimization
Or Zuk, Amnon Amir, Amit Zeisel +2
On the Computational Efficiency of Training Neural Networks
Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
Distribution-Specific Hardness of Learning Neural Networks
Ohad Shamir
Convergence of Stochastic Gradient Descent for PCA
Ohad Shamir
Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity
Sham M. Kakade, Ohad Shamir, Karthik Sridharan +1
Adaptively Learning the Crowd Kernel
Omer Tamuz, Ce Liu, Serge Belongie +2
The Complexity of Making the Gradient Small in Stochastic Convex Optimization
Dylan J. Foster, Ayush Sekhari, Ohad Shamir +3
Attribute Efficient Linear Regression with Data-Dependent Sampling
Doron Kukliansky, Ohad Shamir
Oracle Complexity of Second-Order Methods for Finite-Sum Problems
Yossi Arjevani, Ohad Shamir
Better Mini-Batch Algorithms via Accelerated Gradient Methods
Andrew Cotter, Ohad Shamir, Nathan Srebro +1
Online Learning of Noisy Data with Kernels
Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir
Decoupling Exploration and Exploitation in Multi-Armed Bandits
Orly Avner, Shie Mannor, Ohad Shamir
An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback
Ohad Shamir
Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback
Noga Alon, Nicolò Cesa-Bianchi, Claudio Gentile +3
Exponential Convergence Time of Gradient Descent for One-Dimensional Deep Linear Neural Networks
Ohad Shamir
A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates
Yossi Arjevani, Ohad Shamir, Nathan Srebro
On the Complexity of Bandit Linear Optimization
Ohad Shamir
Oracle Complexity of Second-Order Methods for Smooth Convex Optimization
Yossi Arjevani, Ohad Shamir, Ron Shiff