Active Learning for Accurate Estimation of Linear Models

ICML 2017 Carlos RiquelmeMohammad GhavamzadehAlessandro Lazaric

We explore the sequential decision making problem where the goal is to estimate uniformly well a number of linear models, given a shared budget of random contexts independently sampled from a known distribution. The decision maker must query one of the linear models for each incoming context, and receives an observation corrupted by noise levels that are unknown, and depend on the model instance... (read more)

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