Projects per year
Time series models such as dynamical systems are frequently fitted to a cohort of data, ignoring variation between individual entities such as patients. In this paper we show how these models can be personalised to an individual level while retaining statistical power, via use of multi-task learning (MTL). To our knowledge this is a novel development of MTL which applies to time series both with and without control inputs. The modelling framework is demonstrated on a physiological drug response problem which results in improved predictive accuracy and uncertainty estimation over existing state-of-the-art models.
|Title of host publication||Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics|
|Editors||Neil Lawrence, Mark Reid|
|Number of pages||10|
|Publication status||E-pub ahead of print - 18 Apr 2019|
|Event||22nd International Conference on Artificial Intelligence and Statistics - Naha, Japan|
Duration: 16 Apr 2019 → 18 Apr 2019
|Name||Proceedings of Machine Learning Research|
|Conference||22nd International Conference on Artificial Intelligence and Statistics|
|Abbreviated title||AISTATS 2019|
|Period||16/04/19 → 18/04/19|
FingerprintDive into the research topics of 'Multi-Task Time Series Analysis applied to Drug Response Modelling'. Together they form a unique fingerprint.
- 2 Finished