We introduce a new model for learning in the presence of noise, which we call the Nasty Noise model. This model generalizes previously considered models of learning with noise. Th...
This paper addresses the question of producing modular sequential imperative code from synchronous data-flow networks. Precisely, given a system with several input and output flow...
In this paper we consider the problem of routing packets to a single destination in a dynamically changing network, where both the network and the packet injections are under adve...
Private information retrieval (PIR) enables a user to retrieve a data item from a database, replicated among one or more servers, while hiding the identity of the retrieved item. ...
The central goal of data stream algorithms is to process massive streams of data using sublinear storage space. Motivated by work in the database community on outsourcing database...