
Depth Separations in Neural Networks: What is Actually Being Separated?
Existing depth separation results for constantdepth networks essentiall...
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Proving the Lottery Ticket Hypothesis: Pruning is All You Need
The lottery ticket hypothesis (Frankle and Carbin, 2018), states that a ...
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How Good is SGD with Random Shuffling?
We study the performance of stochastic gradient descent (SGD) on smooth ...
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Is Local SGD Better than Minibatch SGD?
We study local SGD (also known as parallel SGD and federated averaging),...
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Bandit Regret Scaling with the Effective Loss Range
We study how the regret guarantees of nonstochastic multiarmed bandits ...
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Failures of GradientBased Deep Learning
In recent years, Deep Learning has become the goto solution for a broad...
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Oracle Complexity of SecondOrder Methods for FiniteSum Problems
Finitesum optimization problems are ubiquitous in machine learning, and...
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DepthWidth Tradeoffs in Approximating Natural Functions with Neural Networks
We provide several new depthbased separation results for feedforward n...
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DistributionSpecific Hardness of Learning Neural Networks
Although neural networks are routinely and successfully trained in pract...
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Spurious Local Minima are Common in TwoLayer ReLU Neural Networks
We consider the optimization problem associated with training simple ReL...
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WithoutReplacement Sampling for Stochastic Gradient Methods: Convergence Results and Application to Distributed Optimization
Stochastic gradient methods for machine learning and optimization proble...
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The Power of Depth for Feedforward Neural Networks
We show that there is a simple (approximately radial) function on ^d, ex...
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MultiPlayer Bandits  a Musical Chairs Approach
We consider a variant of the stochastic multiarmed bandit problem, wher...
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On the Quality of the Initial Basin in Overspecified Neural Networks
Deep learning, in the form of artificial neural networks, has achieved r...
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Convergence of Stochastic Gradient Descent for PCA
We consider the problem of principal component analysis (PCA) in a strea...
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Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity
We study the convergence properties of the VRPCA algorithm introduced b...
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An Optimal Algorithm for Bandit and ZeroOrder Convex Optimization with TwoPoint Feedback
We consider the closely related problems of bandit convex optimization w...
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Communication Complexity of Distributed Convex Learning and Optimization
We study the fundamental limits to communicationefficient distributed m...
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On the Complexity of Learning with Kernels
A wellrecognized limitation of kernel learning is the requirement to ha...
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Attribute Efficient Linear Regression with DataDependent Sampling
In this paper we analyze a budgeted learning setting, in which the learn...
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On the Computational Efficiency of Training Neural Networks
It is wellknown that neural networks are computationally hard to train....
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Nonstochastic MultiArmed Bandits with GraphStructured Feedback
We present and study a partialinformation model of online learning, whe...
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Communication Efficient Distributed Optimization using an Approximate Newtontype Method
We present a novel Newtontype method for distributed optimization, whic...
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Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation
Many machine learning approaches are characterized by information constr...
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An Algorithm for Training Polynomial Networks
We consider deep neural networks, in which the output of each node is a ...
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Online Learning with Switching Costs and Other Adaptive Adversaries
We study the power of different types of adaptive (nonoblivious) adversa...
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Stochastic Gradient Descent for Nonsmooth Optimization: Convergence Results and Optimal Averaging Schemes
Stochastic Gradient Descent (SGD) is one of the simplest and most popula...
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On the Complexity of Bandit and DerivativeFree Stochastic Convex Optimization
The problem of stochastic convex optimization with bandit feedback (in t...
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Relax and Localize: From Value to Algorithms
We show a principled way of deriving online learning algorithms from a m...
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Learning with the Weighted Tracenorm under Arbitrary Sampling Distributions
We provide rigorous guarantees on learning with the weighted tracenorm ...
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From Bandits to Experts: On the Value of SideObservations
We consider an adversarial online learning setting where a decision make...
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Efficient Transductive Online Learning via Randomized Rounding
Most traditional online learning algorithms are based on variants of mir...
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Graph Approximation and Clustering on a Budget
We consider the problem of learning from a similarity matrix (such as sp...
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LargeScale Convex Minimization with a LowRank Constraint
We address the problem of minimizing a convex function over the space of...
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Using More Data to Speedup Training Time
In many recent applications, data is plentiful. By now, we have a rather...
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Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression
Generalized Linear Models (GLMs) and Single Index Models (SIMs) provide ...
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Learning Exponential Families in HighDimensions: Strong Convexity and Sparsity
The versatility of exponential families, along with their attendant conv...
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SizeIndependent Sample Complexity of Neural Networks
We study the sample complexity of learning neural networks, by providing...
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Detecting Correlations with Little Memory and Communication
We study the problem of identifying correlations in multivariate data, u...
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Are ResNets Provably Better than Linear Predictors?
A residual network (or ResNet) is a standard deep neural net architectur...
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A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates
We provide tight finitetime convergence bounds for gradient descent and...
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Exponential Convergence Time of Gradient Descent for OneDimensional Deep Linear Neural Networks
In this note, we study the dynamics of gradient descent on objective fun...
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The Complexity of Making the Gradient Small in Stochastic Convex Optimization
We give nearly matching upper and lower bounds on the oracle complexity ...
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Space lower bounds for linear prediction
We show that fundamental learning tasks, such as finding an approximate ...
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Global Nonconvex Optimization with Discretized Diffusions
An Euler discretization of the Langevin diffusion is known to converge t...
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On the Power and Limitations of Random Features for Understanding Neural Networks
Recently, a spate of papers have provided positive theoretical results f...
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The Complexity of Finding Stationary Points with Stochastic Gradient Descent
We study the iteration complexity of stochastic gradient descent (SGD) f...
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Learning a Single Neuron with Gradient Methods
We consider the fundamental problem of learning a single neuron x σ(w^ x...
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Ohad Shamir
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Faculty member Department of Computer Science and Applied Mathematics at Weizmann Institute of Science