
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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GRANDPA: a Byzantine Finality Gadget
Classic Byzantine faulttolerant consensus protocols forfeit liveness in...
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Overview of Polkadot and its Design Considerations
In this paper we describe the design components of the heterogenous mult...
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Validator election in nominated proofofstake
Polkadot is a decentralized blockchain platform to be launched in 2020. ...
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OutlierRobust HighDimensional Sparse Estimation via Iterative Filtering
We study highdimensional sparse estimation tasks in a robust setting wh...
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A Polynomial Time Algorithm for LogConcave Maximum Likelihood via Locally Exponential Families
We consider the problem of computing the maximum likelihood multivariate...
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A Polynomial Time Algorithm for Maximum Likelihood Estimation of Multivariate Logconcave Densities
We study the problem of computing the maximum likelihood estimator (MLE)...
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Reachability for Branching Concurrent Stochastic Games
We give polynomial time algorithms for deciding almostsure and limitsu...
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Efficient Algorithms and Lower Bounds for Robust Linear Regression
We study the problem of highdimensional linear regression in a robust m...
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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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NearOptimal Sample Complexity Bounds for Maximum Likelihood Estimation of Multivariate Logconcave Densities
We study the problem of learning multivariate logconcave densities with...
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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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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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Robust Learning of FixedStructure Bayesian Networks
We investigate the problem of learning Bayesian networks in an agnostic ...
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Robust Estimators in High Dimensions without the Computational Intractability
We study highdimensional distribution learning in an agnostic setting w...
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Alistair Stewart
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