Constrained likelihood for reconstructing a directed acyclic Gaussian graph

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...

Maximum Likelihood Estimation Over Directed Acyclic Gaussian Graphs

Estimation of multiple directed graphs becomes challenging in the presence of inhomogeneous data, where directed acyclic graphs (DAGs) are used to represent causal relations among random variables. To infer causal relations among variables, we estimate multiple DAGs given a known ordering in Gaussian graphical models. In particular, we propose a constrained maximum likelihood method with noncon...

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...

Directed Acyclic Subsequence Graph for multiple texts

The subsequence matching problem is to decide, for given strings S and T , whether S is a subsequence of T. The string S is called pattern and the string T text. We consider the case of multiple texts and show how to solve the subsequence matching problem in time linear in the length of pattern. For this purpose we build the automaton that accepts all subsequences of given texts. The automaton ...

Explaining Regime Change : a Directed Acyclic Graph