Perhaps no other topic in risk analysis is more difficult, more controversial, or more important
to risk management policy analysts and decision-makers than how to draw valid, correctly …

In applications of domain adaptation, there may exist multiple source domains, which can
provide more or less complementary knowledge for pattern classification in the target …

For an AI agent to make trustworthy decision recommendations under uncertainty on behalf
of human principals, it should be able to explain why its recommended decisions make …

This paper investigates online stochastic planning for problems with large factored state and
action spaces. One promising approach in recent work estimates the quality of applicable …

We show how to integrate a variable elimination approach to solving influence diagrams
with a value iteration approach to solving finite-horizon partially observable Markov decision …

For an AI agent to make trustworthy decision recommendations under uncertainty on behalf
of human principals, it should be able to explain why its recommended decisions make …

Influence diagrams provide a modeling and inference framework for sequential decision
problems, representing the probabilistic knowledge by a Bayesian network and the …

Mixed inference such as the marginal MAP query (some variables marginalized by
summation and others by maximization) is key to many prediction and decision models. It is …

Considering the complexity and uncertainty of decision‐making in the operating
environment of an unmanned underwater vehicle (UUV), this study proposes an …

Sum-product networks are expressive efficient probabilistic graphical models that allow for
tractable marginal inference. Many tasks however require the computation of maximum-a …