The bias-variance decomposition is a very useful and widely-used tool for understanding machine-learning algorithms. It was originally developed for squared loss. In recent years,...
Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect ...
When dealing with signals from complex environments, where multiple time-dependent signal signatures can interfere with each other in stochastically unpredictable ways, traditiona...
Transactional standards have been promoted by OMG and X/Open to allow heterogeneous resources to participate in an Atomic Commitment Protocol (ACP), namely the two-phase commit pro...
Implementing new operating systems is tedious, costly, and often impractical except for large projects. The Flux OSKit addresses this problem in a novel way by providing clean, we...
Bryan Ford, Godmar Back, Greg Benson, Jay Lepreau,...