no code implementations • 12 Jun 2020 • Yujia Shen, Arthur Choi, Adnan Darwiche
We propose to first learn a functional and parameterized representation of a conditional probability table (CPT), such as a neural network.
no code implementations • NeurIPS 2017 • Arthur Choi, Yujia Shen, Adnan Darwiche
Recently, the Probabilistic Sentential Decision Diagram (PSDD) has been proposed as a framework for systematically inducing and learning distributions over structured objects, including combinatorial objects such as permutations and rankings, paths and matchings on a graph, etc.
no code implementations • NeurIPS 2016 • Yujia Shen, Arthur Choi, Adnan Darwiche
We consider tractable representations of probability distributions and the polytime operations they support.
no code implementations • NeurIPS 2016 • Eunice Yuh-Jie Chen, Yujia Shen, Arthur Choi, Adnan Darwiche
Our approach is based on a recently proposed framework for optimal structure learning based on non-decomposable scores, which is general enough to accommodate ancestral constraints.
