Online Submodular Maximization is an online optimization problem in which a decision maker sequentially selects elements to maximize a submodular objective function revealed incrementally over time, distinguishing it from offline submodular maximization by requiring irrevocable decisions without full knowledge of future inputs.
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44
The Relaxed Online Maximum Margin Algorithm
Yi Li, Philip M. Long · Machine Learning · 2002 · 230 citations · Full text
Context-aware data quality estimation in mobile crowdsensing
Shengzhong Liu, Zhenzhe Zheng, Fan Wu et al. · 2017 · 89 citations
1–5 of 44
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1–5 of 93
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57
Google (United States)
Mountain View, United States
6 h-index
14 publications
471 citations
University of Hong Kong
Pok Fu Lam, Hong Kong
4 h-index
12 publications
207 citations
Technion – Israel Institute of Technology
Haifa, Israel
4 h-index
4 publications
97 citations
Keio University
Tokyo, Japan
4 h-index
4 publications
68 citations
University of Pennsylvania
Philadelphia, United States
3 h-index
3 publications
78 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
7 publications
159 citations
IBM Research - Almaden
San Jose, United States
3 h-index
3 publications
162 citations
École Normale Supérieure - PSL
Paris, France
3 h-index
3 publications
56 citations
1–8 of 57
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- arXiv (Cornell University)
Repository · 5 publications · 105 citations
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- ACM Transactions on Algorithms
TALG · 2 publications · 36 citations
2
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- SIAM Journal on Computing
2 publications · 58 citations
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- Algorithmica
2 publications · 44 citations
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- CaltechAUTHORS (California Institute of Technology)
Repository · 2 publications · 73 citations
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