We give new algorithms for learning halfspaces in the challenging malicious noise model, where an adversary may corrupt both the labels and the underlying distribution of examples....
Adam R. Klivans, Philip M. Long, Rocco A. Servedio
Writing parallel applications for computational grids is a challenging task. To achieve good performance, algorithms designed for local area networks must be adapted to the differ...
Thilo Kielmann, Rutger F. H. Hofman, Henri E. Bal,...
Abstract. We propose a prediction-based best-effort real-time service to support distributed, interactive applications in shared, unreserved computing environments. These applicati...
Peter A. Dinda, Loukas F. Kallivokas, Bruce Loweka...
In the problem of probability forecasting the learner’s goal is to output, given a training set and a new object, a suitable probability measure on the possible values of the ne...
We present an algorithm for fast posterior inference in penalized high-dimensional state-space models, suitable in the case where a few measurements are taken in each time step. W...