We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse,...
An ensemble is a group of learning models that jointly solve a problem. However, the ensembles generated by existing techniques are sometimes unnecessarily large, which can lead t...
We describe a very general technique for generating families of combinatorial objects without isomorphs. It applies to almost any class of objects for which an inductive construct...
This paper introduces a Combinatory Optimization Problem (COP) which captures the performance in cooperation of a P2P Streaming Network, considered at the buffer level. A new famil...
We propose to solve the combinatorial problem of finding the highest scoring Bayesian network structure from data. This structure learning problem can be viewed as an inference pr...
Tommi Jaakkola, David Sontag, Amir Globerson, Mari...