We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
Abstract. Recent research indicates that prediction-based coherence optimizations offer substantial performance improvements for scientific applications in distributed shared memor...
Stephen Somogyi, Thomas F. Wenisch, Nikolaos Harda...
We investigate the design of iterative, limited-precision mechanisms for single-good auctions with dominant strategy equilibria. Our aim is to design mechanisms that minimize the ...
In this paper, we introduce a workflow-oriented system architecture for the processing of client requests (CRs) for container transportation. In the context of multi-transfer conta...
Learning curves for Gaussian process (GP) regression can be strongly affected by a mismatch between the ‘student’ model and the ‘teacher’ (true data generation process), e...