This paper is concerned with the combinatorial structure of concept classes that can be learned from a small number of examples. We show that the recently introduced notion of recu...
Reasoning with hypothetical cases helps decision-makers evaluate alternate hypotheses for deciding a case. The hypotheticals demonstrate the sensitivity of a hypothesis to apparen...
We present a new approach to semi-supervised anomaly detection. Given a set of training examples believed to come from the same distribution or class, the task is to learn a model ...
This paper studies the input design problem for system identification where time domain constraints have to be considered. A finite Markov chain is used to model the input of the s...
We study the computational and sample complexity of parameter and structure learning in graphical models. Our main result shows that the class of factor graphs with bounded degree...