A New Perspective on Learning Context-Specific Independence
This work addresses the challenge of modeling large complex systems in probabilistic graphical models for researchers and practitioners, but it appears incremental as it builds on existing tools from machine learning and explainable AI.
The paper tackles the problem of learning context-specific independence (CSI) from data by proposing a two-step method: first learning a functional representation of conditional probability tables using neural networks, then quantizing it into an arithmetic circuit for efficient inference. The result is a new approach that contrasts with traditional variable-splitting methods, though no concrete numbers are provided in the abstract.
Local structure such as context-specific independence (CSI) has received much attention in the probabilistic graphical model (PGM) literature, as it facilitates the modeling of large complex systems, as well as for reasoning with them. In this paper, we provide a new perspective on how to learn CSIs from data. We propose to first learn a functional and parameterized representation of a conditional probability table (CPT), such as a neural network. Next, we quantize this continuous function, into an arithmetic circuit representation that facilitates efficient inference. In the first step, we can leverage the many powerful tools that have been developed in the machine learning literature. In the second step, we exploit more recently-developed analytic tools from explainable AI, for the purposes of learning CSIs. Finally, we contrast our approach, empirically and conceptually, with more traditional variable-splitting approaches, that search for CSIs more explicitly.