Name
Abstract | ||
|---|---|---|
Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty due to missingness. Alternatively, state-of-the-art joint modeling techniques c... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TPAMI.2017.2742504 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | DocType | Volume |
Data models,Computational modeling,Predictive models,Reliability,Time series analysis,Uncertainty,Detectors | Journal | 40 |
Issue | ISSN | Citations |
8 | 0162-8828 | 3 |
PageRank | References | Authors |
0.40 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hossein Soleimani | 1 | 10 | 2.60 |
| James Hensman | 2 | 265 | 20.05 |
| Suchi Saria | 3 | 219 | 22.56 |