Header logo is

On Fast Approximate Submodular Minimization

2011

Conference Paper

ei


We are motivated by an application to extract a representative subset of machine learning training data and by the poor empirical performance we observe of the popular minimum norm algorithm. In fact, for our application, minimum norm can have a running time of about O(n7) (O(n5) oracle calls). We therefore propose a fast approximate method to minimize arbitrary submodular functions. For a large sub-class of submodular functions, the algorithm is exact. Other submodular functions are iteratively approximated by tight submodular upper bounds, and then repeatedly optimized. We show theoretical properties, and empirical results suggest significant speedups over minimum norm while retaining higher accuracies.

Author(s): Jegelka, S. and Lin, H. and Bilmes, J.
Book Title: Advances in Neural Information Processing Systems 24
Pages: 460-468
Year: 2011
Day: 0
Editors: J Shawe-Taylor and RS Zemel and P Bartlett and F Pereira and KQ Weinberger

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Digital: 0
Event Name: Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011)
Event Place: Granada, Spain

Links: PDF
Web

BibTex

@inproceedings{JegelkaLB2011,
  title = {On Fast Approximate Submodular Minimization},
  author = {Jegelka, S. and Lin, H. and Bilmes, J.},
  booktitle = {Advances in Neural Information Processing Systems 24},
  pages = {460-468},
  editors = {J Shawe-Taylor and RS Zemel and P Bartlett and F Pereira and KQ Weinberger},
  year = {2011}
}