high probability
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Context-lumpable stochastic bandits
Consider a recommendation platform that interacts with a finite set of users in an online fashion. Users arrive at the platform and receive a recommendation.
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Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Clustering (0.68)
AT Lemmas
Let k (,) be the ν = 2 .5 Matérn kernel.
Error Bounds for Learning with Vector-Valued Random Features
The coefficients in this RF expansion are optimized to fit the given data of input-output pairs.
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Lower Bounds on Adaptive Sensing for Matrix Recovery
We study lower bounds on adaptive sensing algorithms for recovering low rank matrices using linear measurements.
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Minimum-Risk Recalibration of Classifiers
A probabilistic classifier is considered "well calibrated" when its predicted probabilities closely align with the empirical frequencies of the corresponding labels [
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- Information Technology > Artificial Intelligence > Natural Language (0.68)
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- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.68)
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