This paper presents new and effective algorithms for learning kernels. In particular, as shown by our empirical results, these algorithms consistently outperform the so-called uni...
In recent years there has been a lot of interest in designing principled classification algorithms over multiple cues, based on the intuitive notion that using more features shou...
This paper studies issues relating to the parameterization of probability distributions over binary data sets. Several such parameterizations of models for binary data are known, ...
David Buchman, Mark W. Schmidt, Shakir Mohamed, Da...
This document contains supplementary material to the article ‘Statistical test for consistent estimation of causal effects in linear non-Gaussian models’, AISTATS 2012. A tabl...
Models such as pairwise conditional random fields (CRFs) are extremely popular in computer vision and various other machine learning disciplines. However, they have limited expre...