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Uniform Deviation Bounds for k-Means Clustering
In this paper, we provide a novel framework to obtain uniform deviation bounds for loss functions which are unbounded.
Uniform Deviation Bounds for Unbounded Loss Functions like k-Means
In this paper, we provide a novel framework to obtain uniform deviation bounds for loss functions which are *unbounded*.
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests
We consider the Bayesian active learning and experimental design problem, where the goal is to learn the value of some unknown target variable through a sequence of informative, noisy tests.
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