Abstract. Computing tight performance bounds in feed-forward networks under general assumptions about arrival and server models has turned out to be a challenging problem. Recently...
We describe a slightly subexponential time algorithm for learning parity functions in the presence of random classification noise, a problem closely related to several cryptograph...
We present a unified framework for reasoning about worst-case regret bounds for learning algorithms. This framework is based on the theory of duality of convex functions. It brin...
An operation of concatenation is introduced for graphs. Then strings are viewed as expressions denoting graphs, and string languages are interpreted as graph languages. For a clas...
Argumentation is a promising approach used by autonomous agents for reasoning about inconsistent knowledge, based on the construction and the comparison of arguments. In this pape...