
Quantum Money from Quaternion Algebras
We propose a new idea for public key quantum money. In the abstract sens...
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OutlierRobust Sparse Estimation via NonConvex Optimization
We explore the connection between outlierrobust highdimensional statis...
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Threshold Phenomena in Learning Halfspaces with Massart Noise
We study the problem of PAC learning halfspaces on ℝ^d with Massart nois...
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Forster Decomposition and Learning Halfspaces with Noise
A Forster transform is an operation that turns a distribution into one w...
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Statistical Query Lower Bounds for ListDecodable Linear Regression
We study the problem of listdecodable linear regression, where an adver...
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Clustering Mixture Models in AlmostLinear Time via ListDecodable Mean Estimation
We study the problem of listdecodable mean estimation, where an adversa...
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Agnostic Proper Learning of Halfspaces under Gaussian Marginals
We study the problem of agnostically learning halfspaces under the Gauss...
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The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals
We study the problem of agnostic learning under the Gaussian distributio...
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OutlierRobust Learning of Ising Models Under Dobrushin's Condition
We study the problem of learning Ising models satisfying Dobrushin's con...
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The Sample Complexity of Robust Covariance Testing
We study the problem of testing the covariance matrix of a highdimensio...
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Hardness of Learning Halfspaces with Massart Noise
We study the complexity of PAC learning halfspaces in the presence of Ma...
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Small Covers for NearZero Sets of Polynomials and Learning Latent Variable Models
Let V be any vector space of multivariate degreed homogeneous polynomia...
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Robustly Learning Mixtures of k Arbitrary Gaussians
We give a polynomialtime algorithm for the problem of robustly estimati...
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ListDecodable Mean Estimation in NearlyPCA Time
Traditionally, robust statistics has focused on designing estimators tol...
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A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise
We study the problem of PAC learning homogeneous halfspaces in the prese...
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Prisoners, Rooms, and Lightswitches
We examine a new variant of the classic prisoners and lightswitches puzz...
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Optimal Testing of Discrete Distributions with High Probability
We study the problem of testing discrete distributions with a focus on t...
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Outlier Robust Mean Estimation with Subgaussian Rates via Stability
We study the problem of outlier robust highdimensional mean estimation ...
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The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise
We study the computational complexity of adversarially robust proper lea...
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Robust Learning of Mixtures of Gaussians
We resolve one of the major outstanding problems in robust statistics. I...
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NearOptimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals
We study the fundamental problems of agnostically learning halfspaces an...
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Algorithms and SQ Lower Bounds for PAC Learning OneHiddenLayer ReLU Networks
We study the problem of PAC learning onehiddenlayer ReLU networks with...
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ListDecodable Mean Estimation via Iterative MultiFiltering
We study the problem of listdecodable mean estimation for bounded covar...
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ListDecodable Mean Estimation via Iterative MultiFitering
We study the problem of listdecodable mean estimation for bounded cova...
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Point Location and Active Learning: Learning Halfspaces Almost Optimally
Given a finite set X ⊂ℝ^d and a binary linear classifier c: ℝ^d →{0,1}, ...
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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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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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Communication and Memory Efficient Testing of Discrete Distributions
We study distribution testing with communication and memory constraints ...
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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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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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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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Testing Identity of Multidimensional Histograms
We investigate the problem of identity testing for multidimensional hist...
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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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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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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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On Communication Complexity of Classification Problems
This work introduces a model of distributed learning in the spirit of Ya...
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Learning Geometric Concepts with Nasty Noise
We study the efficient learnability of geometric concept classes  speci...
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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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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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Daniel M. Kane
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Dir. of Media Relations and Public Affairs at UC San Diego Jacobs School of Engineering