Reviews: Towards Hardware-Aware Tractable Learning of Probabilistic Models

The authors propose a method to trade-off "computational costs" and "model fit" when learning a Sum-Product-Network (SPNs) represented as an Arithmetic Circuit. An SPN is a compact representation of a probabilistic model over discrete random variables with finite domain. The proposed method involves an SPN learner that is restricted to binary random variables. In practice, this requires to convert continuous variables into categoricals (e.g., using binning), and categoricals into binaries. While SPNs can handle missing data, they do are typically black-box models where the structure is learned.