
On the Computational Efficiency of Training Neural Networks
It is wellknown that neural networks are computationally hard to train....
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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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On Communication Complexity of Classification Problems
This work introduces a model of distributed learning in the spirit of Ya...
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Private PAC learning implies finite Littlestone dimension
We show that every approximately differentially private learning algorit...
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Passing Tests without Memorizing: Two Models for Fooling Discriminators
We introduce two mathematical frameworks for foolability in the context ...
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On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes
We study the expressive power of kernel methods and the algorithmic feas...
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Graphbased Discriminators: Sample Complexity and Expressiveness
A basic question in learning theory is to identify if two distributions ...
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Prediction with Corrupted Expert Advice
We revisit the fundamental problem of prediction with expert advice, in ...
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Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study
The notion of implicit bias, or implicit regularization, has been sugges...
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An Equivalence Between Private Classification and Online Prediction
We prove that every concept class with finite Littlestone dimension can ...
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