
Statistical Estimation from Dependent Data
We consider a general statistical estimation problem wherein binary labe...
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Majorizing Measures, Sequential Complexities, and Online Learning
We introduce the technique of generic chaining and majorizing measures f...
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Adversarial Laws of Large Numbers and Optimal Regret in Online Classification
Laws of large numbers guarantee that given a large enough sample from so...
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A boundednoise mechanism for differential privacy
Answering multiple counting queries is one of the beststudied problems ...
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Estimating Ising Models from One Sample
Given one sample X ∈{± 1}^n from an Ising model [X=x]∝(x^ J x/2), whose ...
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PAC learning with stable and private predictions
We study binary classification algorithms for which the prediction on an...
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Interaction is necessary for distributed learning with privacy or communication constraints
Local differential privacy (LDP) is a model where users send privatized ...
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Learning from weakly dependent data under Dobrushin's condition
Statistical learning theory has largely focused on learning and generali...
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The LogConcave Maximum Likelihood Estimator is Optimal in High Dimensions
We study the problem of learning a ddimensional logconcave distributio...
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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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The entropy of lies: playing twenty questions with a liar
`Twenty questions' is a guessing game played by two players: Bob thinks ...
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A Better Resource Allocation Algorithm with SemiBandit Feedback
We study a sequential resource allocation problem between a fixed number...
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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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Yuval Dagan
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