We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
Learning reusable sequences can support the development of expertise in many domains, either by improving decisionmaking quality or decreasing execution speed. This paper introduc...
We study the mergeability of data summaries. Informally speaking, mergeability requires that, given two summaries on two data sets, there is a way to merge the two summaries into ...
Pankaj K. Agarwal, Graham Cormode, Zengfeng Huang,...
Though adjustable autonomy is hardly a new topic in agent systems, there has been a general lack of consensus on terminology and basic concepts. In this paper, we describe the mul...
Jeffrey M. Bradshaw, Hyuckchul Jung, Shriniwas Kul...
One common predictive modeling challenge occurs in text mining problems is that the training data and the operational (testing) data are drawn from different underlying distributi...