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Suming Chen

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AAAI Conference 2015 Conference Paper

Value of Information Based on Decision Robustness

Suming Chen
Arthur Choi
Adnan Darwiche

There are many criteria for measuring the value of information (VOI), each based on a different principle that is usually suitable for specific applications. We propose a new criterion for measuring the value of information, which values information that leads to robust decisions (i. e. , ones that are unlikely to change due to new information). We also introduce an algorithm for Naive Bayes networks that selects features with maximal VOI under the new criterion. We discuss the application of the new criterion to classification tasks, showing how it can be used to tradeoff the budget, allotted for acquiring information, with the classification accuracy. In particular, we show empirically that the new criterion can reduce the expended budget significantly while reducing the classification accuracy only slightly. We also show empirically that the new criterion leads to decisions that are much more robust than those based on traditional VOI criteria, such as information gain and classification loss. This make the new criterion particularly suitable for certain decision making applications.

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IJCAI Conference 2013 Conference Paper

An Exact Algorithm for Computing the Same-Decision Probability

Suming Chen
Arthur Choi
Adnan Darwiche

When using graphical models for decision making, the presence of unobserved variables may hinder our ability to reach the correct decision. A fundamental question here is whether or not one is ready to make a decision (stopping criteria), and if not, what additional observations should be made in order to better prepare for a decision (selection criteria). A recently introduced notion, the Same- Decision Probability (SDP), has been shown to be useful as both a stopping and a selection criteria. This query has been shown to be highly intractable, being PPPP –complete, and is exemplary of a class of queries which correspond to the computation of certain expectations. We propose the first exact algorithm for computing the SDP in this paper, and demonstrate its effectiveness on several real and synthetic networks. We also present a new complexity result for computing the SDP on models with a Naive Bayes structure.

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