Name
Abstract | ||
|---|---|---|
Coverage functions are an important class of discrete functions that capture the law of diminishing returns arising naturally from applications in social network analysis, machine learning, and algorithmic game theory. In this paper, we propose a new problem of learning time-varying coverage functions, and develop a novel parametrization of these functions using random features. Based on the connection between time-varying coverage functions and counting processes, we also propose an efficient parameter learning algorithm based on likelihood maximization, and provide a sample complexity analysis. We applied our algorithm to the influence function estimation problem in information diffusion in social networks, and show that with few assumptions about the diffusion processes, our algorithm is able to estimate influence significantly more accurately than existing approaches on both synthetic and real world data. |
Year | Venue | Field |
|---|---|---|
2014 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 27 (NIPS 2014) | Maximum coverage problem,Mathematical optimization,Social network,Parametrization,Computer science,Social network analysis,Algorithmic game theory,Artificial intelligence,Discrete functions,Diminishing returns,Sample complexity,Machine learning |
DocType | Volume | ISSN |
Conference | 27 | 1049-5258 |
Citations | PageRank | References |
2 | 0.35 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nan Du | 1 | 503 | 52.49 |
| Yingyu Liang | 2 | 393 | 31.39 |
| Maria-Florina Balcan | 3 | 1445 | 105.01 |
| Le Song | 4 | 2437 | 159.27 |