Current explanation-based generalization (EBG) techniques can perform badly when the problem being solved involves recursion. Often an infinite series of learned concepts are gene...
We present a probabilistic model for clustering of objects represented via pairwise dissimilarities. We propose that even if an underlying vectorial representation exists, it is b...
Julia E. Vogt, Sandhya Prabhakaran, Thomas J. Fuch...
Approximate MAP inference in graphical models is an important and challenging problem for many domains including computer vision, computational biology and natural language unders...
Optimization problems with a nuclear norm regularization, such as e.g. low norm matrix factorizations, have seen many applications recently. We propose a new approximation algorit...
Prediction markets are used in real life to predict outcomes of interest such as presidential elections. In this work we introduce a mathematical theory for Artificial Prediction ...