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Data Representation and Compression Using Linear-Programming Approximations
We also discuss how to derive features from the compressed documents and show that while certain unregularized linear models are invariant to the structure of the compressed dictionary, this structure may be used to regularize learning.
Learning Mixed Graphical Models
We present a new pairwise model for graphical models with both continuous and discrete variables that is amenable to structure learning.
One sketch for all: Theory and Application of Conditional Random Sampling
Conditional Random Sampling (CRS) was originally proposed for efficiently computing pairwise ($l_2$, $l_1$) distances, in static, large-scale, and sparse data sets such as text and Web data.
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