Active Learning in Discrete Input Spaces

Abstract

Traditional design of experiments (DOE) from the statistics literature focuses on optimizing an output parameter over a space of continuous input parameters. Here we consider DOE, or active learning, for discrete input spaces. A trivial example of this is the k-armed bandit problem, which is the case of having a single input attribute of arity k. We address the full problem of many attributes where it is impossible to test every combination of attribute-value pairs even once within the given number of experiments, but we expect to be able to generalize on the results of experiments.