Online submodular minimization for combinatorial structures
2011
Conference Paper
ei
Most results for online decision problems with structured concepts, such as trees or cuts, assume linear costs. In many settings, however, nonlinear costs are more realistic. Owing to their non-separability, these lead to much harder optimization problems. Going beyond linearity, we address online approximation algorithms for structured concepts that allow the cost to be submodular, i.e., nonseparable. In particular, we show regret bounds for three Hannan-consistent strategies that capture different settings. Our results also tighten a regret bound for unconstrained online submodular minimization.
Author(s): | Jegelka, S. and Bilmes, J. |
Pages: | 345-352 |
Year: | 2011 |
Month: | July |
Day: | 0 |
Editors: | Getoor, L. , T. Scheffer |
Publisher: | International Machine Learning Society |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | 28th International Conference on Machine Learning (ICML 2011) |
Event Place: | Bellevue, WA, USA |
Address: | Madison, WI, USA |
Digital: | 0 |
ISBN: | 978-1-450-30619-5 |
Links: |
PDF
Web |
BibTex @inproceedings{JegelkaB2011_3, title = {Online submodular minimization for combinatorial structures}, author = {Jegelka, S. and Bilmes, J.}, pages = {345-352}, editors = {Getoor, L. , T. Scheffer}, publisher = {International Machine Learning Society}, address = {Madison, WI, USA}, month = jul, year = {2011}, doi = {}, month_numeric = {7} } |