no code implementations • NeurIPS 2010 • Anton Chechetka, Carlos Guestrin
We present a simple and effective approach to learning tractable conditional random fields with structure that depends on the evidence.
no code implementations • NeurIPS 2007 • Anton Chechetka, Carlos Guestrin
We present the first truly polynomial algorithm for learning the structure of bounded-treewidth junction trees -- an attractive subclass of probabilistic graphical models that permits both the compact representation of probability distributions and efficient exact inference.