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Similarity-based learning assumes one is given similarities between samples to learn from, and can be considered a special case of graph-based learning where the graph is given and fully-connected. Such problems arise frequently in computer vision, bioinformatics, and problems involving human judgme ...

Although the Dirichlet distribution is widely used, the independence structure of its components limits its accuracy as a model. The proposed shadow Dirichlet distribution manipulates the support in order to model probability mass functions (pmfs) with dependencies or constraints that often arise in ...

Many computational photography applications can be framed as low-dimensional regression problems that require fast evaluation of test samples for rendering. In such cases, storing samples on a grid or lattice that can be quickly interpolated is often a practical approach. We show how to optimally so ...