166 citations · 1.4k across the 47 of their papers we have counts for
11 papers · 1 filter
On Triangulating Dynamic Graphical Models
Jeff A. Bilmes, Chris Bartels
This paper introduces new methodology to triangulate dynamic Bayesian networks (DBNs) and dynamic graphical models (DGMs). While most methods to triangulate such networks use some…
Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra
Ajit P. Singh, John Halloran, Jeff A. Bilmes +2
Shotgun proteomics is a high-throughput technology used to identify unknown proteins in a complex mixture. At the heart of this process is a prediction task, the spectrum identific…
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-…
Algorithms for Approximate Minimization of the Difference Between Submodular Functions, with Applications
Rishabh Iyer, Jeff Bilmes
We extend the work of Narasimhan and Bilmes [30] for minimizing set functions representable as a difference between submodular functions. Similar to [30], our new algorithms are gu…