166 citations · 1.4k across the 47 of their papers we have counts for
5 papers · 1 filter
Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions
Rishabh Iyer, Stefanie Jegelka, Jeff Bilmes
We investigate three related and important problems connected to machine learning: approximating a submodular function everywhere, learning a submodular function (in a PAC-like set…
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints
Rishabh Iyer, Jeff Bilmes
We investigate two new optimization problems -- minimizing a submodular function subject to a submodular lower bound constraint (submodular cover) and maximizing a submodular funct…
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking
Rishabh Iyer, Jeff Bilmes
We extend the recently introduced theory of Lovasz-Bregman (LB) divergences (Iyer & Bilmes, 2012) in several ways. We show that they represent a distortion between a 'score' and an…
Fast Semidifferential-based Submodular Function Optimization
Rishabh Iyer, Stefanie Jegelka, Jeff Bilmes
We present a practical and powerful new framework for both unconstrained and constrained submodular function optimization based on discrete semidifferentials (sub- and super-differ…
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…