Several studies have pointed out that the values produced by the execution of a program’s instructions are often quite repetitive. There are basically two approaches that have been proposed for exploiting this value locality – 1) reusing the results of a prior execution of an instruction, and 2) predicting the value that will be produced by the current execution of an instruction based on the p...

Motivated by recent developments in manifold-valued regression we propose a family of nonparametric kernel-smoothing estimators with metric-space valued output including several robust versions. Depending on the choice of the output space and the metric the estimator reduces to partially well-known procedures for multi-class classification, multivariate regression in Euclidean space, regression...

In this paper, we propose the problem of domain transfer structured output learning and the first solution to solve it. The problem is defined on two different data domains sharing the same input and output spaces, named as source domain and target domain. The outputs are structured, and for the data samples of the source domain, the corresponding outputs are available, while for most data samp...