We consider the problem of actively learning the mean values of distributions associated with a finite number of options. The decision maker can select which option to generate t...
Creating labeled training data for relation extraction is expensive. In this paper, we study relation extraction in a special weakly-supervised setting when we have only a few see...
This paper investigates a new learning formulation called dynamic group sparsity. It is a natural extension of the standard sparsity concept in compressive sensing, and is motivat...
Measurement and detection of redundancy in test suites attempt to achieve test minimization which in turn can help reduce test maintenance costs, and to also ensure the integrity ...
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this p...