This paper presents a granular computing approach to decision analysis using rough set theory and its variable precision extension. The multiattribute structure of decision domain is mapped to the notions of equivalence relations of rough set theory. It allows expressing decision categories in terms of approximation space wherein a decision class can be approximated through the partition of bou...

Ordered Binary Decision Diagrams are a data structure for representation and manipulation of Boolean functions often applied in VLSI design. The choice of the variable ordering largely influences the size of these structures, size which may vary from polynomial to exponential in the number of variables. A genetic algorithm is applied to find a variable ordering that minimizes the size of ordere...

The prosody of speech is closely related to syntactic structure of the spoken sentence, and thus analysis models that jointly consider these two types of information are promising. However, manual annotation of syntactic information and prosodic information such as pauses is laborious, and thus it can be difficult to obtain sufficient data to train such joint models. In this paper, we tackle th...

Access to Higher Education and Inequality: The Chinese Experiment We apply a semi-parametric latent variable model to estimate selection and sorting effects on the evolution of private returns to schooling for college graduates during China’s reform between 1988 and 2002. We find that there were substantial sorting gains under the traditional system, but they have decreased drastically and beco...

Coactive learning describes the interaction between an online structured learner and a human user who corrects the learner by responding with weak feedback, that is, with an improved, but not necessarily optimal, structure. We apply this framework to discriminative learning in interactive machine translation. We present a generalization to latent variable models and give regret and generalizati...