AI RESEARCH
Efficient Parameter Estimation of Truncated Boolean Product Distributions
arXiv CS.LG
•
ArXi:2007.02392v3 Announce Type: replace We study the problem of estimating the parameters of a Boolean product distribution in $d$ dimensions, when the samples are truncated by a set $S \subset \{0, 1\}^d$ accessible through a membership oracle. This is the first time that the computational and statistical complexity of learning from truncated samples is considered in a discrete setting.