We consider the problem of PAC-learning distributions over strings, represented by probabilistic deterministic finite automata (PDFAs). PDFAs are a probabilistic model for the gen...
Abstract. We demonstrate a set-level approach to the integration of multiple platform gene expression data for predictive classification and show its utility for boosting classi...
Abstract— We consider the problem of planning collisionfree motions for general (i.e., possibly nonholonomic) redundant robots subject to task space constraints. Previous approac...
Abstract. Existing relational learning approaches usually work on complete relational data, but real-world data are often incomplete. This paper proposes the MGDA approach to learn...
We demonstrate that an algorithm proposed by Drineas et. al. in [7] to approximate the singular vectors/values of a matrix A, is not only of theoretical interest but also a fast, v...