Many real-world classification applications fall into the class of positive and unlabeled (PU) learning problems. In many such applications, not only could the negative training ex...
Abstract. Designs of micro electro-mechanical devices need to be robust against fluctuations in mass production. Computer experiments with tens of parameters are used to explore th...
We consider the problem of multi-task reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any g...
Labeled data for classification could often be obtained by sampling that restricts or favors choice of certain classes. A classifier trained using such data will be biased, resulti...
In this paper, we introduce a new, formal model of learning object metadata. The model enables more formal, rigorous reasoning over metadata. An important feature of the model is t...