Learning directed acyclic graph models based on sparsest permutations

Learning directed acyclic graphs based on sparsest permutations

We consider the problem of learning a Bayesian network or directed acyclic graph (DAG) model from observational data. A number of constraint-based, score-based and hybrid algorithms have been developed for this purpose. For constraint-based methods, statistical consistency guarantees typically rely on the faithfulness assumption, which has been show to be restrictive especially for graphs with ...

Analyzing Directed Acyclic Graph Recombination

This work studies the edge-based representation of directed acyclic graphs, as well as the properties of recombination operators working on it. It is shown that this representation is not separable, and the structure of the basic information units that must be processed in order to maintain feasibility of the solutions is described. As an experimental analysis indicates, a recombination operato...

Instrumental variable tests for Directed Acyclic Graph Models

Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction

We study the problem of reducing test-time acquisition costs in classification systems. Our goal is to learn decision rules that adaptively select sensors for each example as necessary to make a confident prediction. We model our system as a directed acyclic graph (DAG) where internal nodes correspond to sensor subsets and decision functions at each node choose whether to acquire a new sensor o...

A Transition-Based Directed Acyclic Graph Parser for UCCA

We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. To our kno...