Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/124881
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Doerr, B. | - |
dc.contributor.author | Doerr, C. | - |
dc.contributor.author | Neumann, A. | - |
dc.contributor.author | Neumann, F. | - |
dc.contributor.author | Sutton, A.M. | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2020, vol.34, iss.2, pp.1460-1467 | - |
dc.identifier.isbn | 9781577358237 | - |
dc.identifier.issn | 2159-5399 | - |
dc.identifier.issn | 2374-3468 | - |
dc.identifier.uri | http://hdl.handle.net/2440/124881 | - |
dc.description | AAAI-20 Technical Tracks 2 / AAAI Technical Track: Constraint Satisfaction and Optimization | - |
dc.description.abstract | Submodular optimization plays a key role in many real-world problems. In many real-world scenarios, it is also necessary to handle uncertainty, and potentially disruptive events that violate constraints in stochastic settings need to be avoided. In this paper, we investigate submodular optimization problems with chance constraints. We provide a first analysis on the approximation behavior of popular greedy algorithms for submodular problems with chance constraints. Our results show that these algorithms are highly effective when using surrogate functions that estimate constraint violations based on Chernoff bounds. Furthermore, we investigate the behavior of the algorithms on popular social network problems and show that high quality solutions can still be obtained even if there are strong restrictions imposed by the chance constraint. | - |
dc.description.statementofresponsibility | Benjamin Doerr, Carola Doerr, Aneta Neumann, Frank Neumann, Andrew M. Sutton | - |
dc.language.iso | en | - |
dc.publisher | Association for the Advancement of Artificial Intelligence | - |
dc.relation.ispartofseries | AAAI Conference on Artificial Intelligence | - |
dc.rights | Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. | - |
dc.source.uri | https://aaai.org/Library/AAAI/aaai20contents.php | - |
dc.title | Optimization of chance-constrained submodular functions | - |
dc.type | Conference paper | - |
dc.contributor.conference | Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI) (7 Feb 2020 - 12 Feb 2020 : New York, NY, USA) | - |
dc.identifier.doi | 10.1609/aaai.v34i02.5504 | - |
dc.publisher.place | Palo Alto, CA | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP160102401 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Neumann, A. [0000-0002-0036-4782] | - |
dc.identifier.orcid | Neumann, F. [0000-0002-2721-3618] | - |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.