Cluster ensembles provide a framework for combining multiple base clusterings of a dataset to generate a stable and robust consensus clustering. There are important variants of th...
Abstract. We consider the clustering with diversity problem: given a set of colored points in a metric space, partition them into clusters such that each cluster has at least point...
We consider the problem belief-state monitoring for the purposes of implementing a policy for a partially-observable Markov decision process (POMDP), specifically how one might ap...
We present a new estimator for counting the number of solutions of a Boolean satisfiability problem as a part of an importance sampling framework. The estimator uses the recently...
Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. This p...