127 citations · 399 across the 8 of their papers we have counts for
5 papers · 1 filter
Dynamic Bayesian Multinets
Jeff A. Bilmes
In this work, dynamic Bayesian multinets are introduced where a Markov chain state at time t determines conditional independence patterns between random variables lying within a lo…
Learning Mixtures of Submodular Shells with Application to Document Summarization
Hui Lin, Jeff A. Bilmes
We introduce a method to learn a mixture of submodular "shells" in a large-margin setting. A submodular shell is an abstract submodular function that can be instantiated with a gro…
PAC-learning bounded tree-width Graphical Models
Mukund Narasimhan, Jeff A. Bilmes
We show that the class of strongly connected graphical models with treewidth at most k can be properly efficiently PAC-learnt with respect to the Kullback-Leibler Divergence. Previ…
A submodular-supermodular procedure with applications to discriminative structure learning
Mukund Narasimhan, Jeff A. Bilmes
In this paper, we present an algorithm for minimizing the difference between two submodular functions using a variational framework which is based on (an extension of) the concave-…
Average-Case Active Learning with Costs
Andrew Guillory, Jeff Bilmes
We analyze the expected cost of a greedy active learning algorithm. Our analysis extends previous work to a more general setting in which different queries have different costs. Mo…