35 citations · 151 across the 13 of their papers we have counts for
15 papers
On Symbolically Encoding the Behavior of Random Forests
Arthur Choi, Andy Shih, Anchal Goyanka +1
Recent work has shown that the input-output behavior of some machine learning systems can be captured symbolically using Boolean expressions or tractable Boolean circuits, which fa…
A New Perspective on Learning Context-Specific Independence
Yujia Shen, Arthur Choi, Adnan Darwiche
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 lar…
On Tractable Representations of Binary Neural Networks
Weijia Shi, Andy Shih, Adnan Darwiche +1
We consider the compilation of a binary neural network's decision function into tractable representations such as Ordered Binary Decision Diagrams (OBDDs) and Sentential Decision D…
On the Relative Expressiveness of Bayesian and Neural Networks
Arthur Choi, Ruocheng Wang, Adnan Darwiche
A neural network computes a function. A central property of neural networks is that they are "universal approximators:" for a given continuous function, there exists a neural netwo…
A Symbolic Approach to Explaining Bayesian Network Classifiers
Andy Shih, Arthur Choi, Adnan Darwiche
We propose an approach for explaining Bayesian network classifiers, which is based on compiling such classifiers into decision functions that have a tractable and symbolic form. We…
On Relaxing Determinism in Arithmetic Circuits
Arthur Choi, Adnan Darwiche
The past decade has seen a significant interest in learning tractable probabilistic representations. Arithmetic circuits (ACs) were among the first proposed tractable representatio…