What cannot be learned with Bethe Approximations
published: Jan. 16, 2013, recorded: December 2012, views: 3658
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its Bethe approximation. However not much is known about the theoretical properties of such approximations. Here we show that there exists a regime of empirical marginals where such "Bethe learning" will fail. By failure we mean that moment matching will not be achieved. We provide several conditions on empirical marginals that yield outer and inner bounds on the set of Bethe learnable marginals. An interesting implication of our results is that there exists a large class of marginals that cannot be obtained as stable fixed points of belief propagation. Taken together our results provide a novel approach to analyzing learning with Bethe approximations and highlight when it can be expected to work or fail.
Download slides: nipsworkshops2012_globerson_bethe_approximations_01.pdf (1.2 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !