
Recent Advances in Algorithmic HighDimensional Robust Statistics
Learning in the presence of outliers is a fundamental problem in statist...
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Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin
We study the problem of properly learning large margin halfspaces in th...
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Learning Ising Models with Independent Failures
We give the first efficient algorithm for learning the structure of an I...
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The Power of Comparisons for Actively Learning Linear Classifiers
In the world of big data, large but costly to label datasets dominate ma...
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SuperLinear Gate and SuperQuadratic Wire Lower Bounds for DepthTwo and DepthThree Threshold Circuits
In order to formally understand the power of neural computing, we first ...
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Robustly Learning a Gaussian: Getting Optimal Error, Efficiently
We study the fundamental problem of learning the parameters of a highdi...
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Being Robust (in High Dimensions) Can Be Practical
Robust estimation is much more challenging in high dimensions than it is...
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Learning Geometric Concepts with Nasty Noise
We study the efficient learnability of geometric concept classes  speci...
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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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Sever: A Robust MetaAlgorithm for Stochastic Optimization
In high dimensions, most machine learning methods are brittle to even a ...
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ListDecodable Robust Mean Estimation and Learning Mixtures of Spherical Gaussians
We study the problem of listdecodable Gaussian mean estimation and the ...
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Waring's Theorem for Binary Powers
A natural number is a binary k'th power if its binary representation con...
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Testing Conditional Independence of Discrete Distributions
We study the problem of testing conditional independence for discrete di...
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Testing Identity of Multidimensional Histograms
We investigate the problem of identity testing for multidimensional hist...
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Quantum Money from Modular Forms
We present a new idea for a class of public key quantum money protocols ...
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Generalized comparison trees for pointlocation problems
Let H be an arbitrary family of hyperplanes in ddimensions. We show th...
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Degreed Chow Parameters Robustly Determine Degreed PTFs (and Algorithmic Applications)
The degreed Chow parameters of a Boolean function f: {1,1}^n →R are it...
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Communication and Memory Efficient Testing of Discrete Distributions
We study distribution testing with communication and memory constraints ...
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Daniel M. Kane
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Dir. of Media Relations and Public Affairs at UC San Diego Jacobs School of Engineering