We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Abstract--Learning multiple related tasks from data simultaneously can improve predictive performance relative to learning these tasks independently. In this paper we propose a nov...
Jean Baptiste Faddoul, Boris Chidlovskii, Fabien T...
We consider the problem of learning sparse parities in the presence of noise. For learning parities on r out of n variables, we give an algorithm that runs in time poly log 1 δ , ...
Abstract. Modern multiprocessor architectures such as CC-NUMA machines or CMPs have nonuniform communication architectures that render programs sensitive to memory access locality....
Abstract. Partitioned Global Address Space (PGAS) languages offer an attractive, high-productivity programming model for programming large-scale parallel machines. PGAS languages, ...
Christopher Barton, Calin Cascaval, George Alm&aac...