We study a refined framwork of parameterized complexity theory where the parameter dependendence of fixed-parameter tractable algorithms is not arbitrary, but restricted by a fu...
We consider learning in situations where the function used to classify examples may switch back and forth between a small number of different concepts during the course of learnin...
The C programming language is at least as well known for its absence of spatial memory safety guarantees (i.e., lack of bounds checking) as it is for its high performance. C'...
Joe Devietti, Colin Blundell, Milo M. K. Martin, S...
The notion of algorithmic stability has been used effectively in the past to derive tight generalization bounds. A key advantage of these bounds is that they are designed for spec...
The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One ...