Active Learning for Non-Parametric Choice Models

arXiv:2208.03346v2 Announce Type: replace
Abstract: We study the problem of actively learning a non-parametric choice model based on consumers' decisions. We present a negative result showing that such choice models may not be identifiable. To overcome the identifiability problem, we introduce a directed acyclic graph (DAG) representation of the choice model. This representation provably encodes all the information about the choice model which can be inferred from the available data, in the sense that it permits computing all choice probabilities. …

abstract active learning arxiv consumers cs.ds cs.lg dag decisions graph math.oc math.pr negative non-parametric parametric representation stat.ml study type