Population-aware Hierarchical Bayesian Domain Adaptation

Autor:	Mhasawade, Vishwali, Rehman, Nabeel Abdur, Chunara, Rumi
Rok vydání:	2018
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Population attributes are essential in health for understanding who the data represents and precision medicine efforts. Even within disease infection labels, patients can exhibit significant variability; "fever" may mean something different when reported in a doctor's office versus from an online app, precluding directly learning across different datasets for the same prediction task. This problem falls into the domain adaptation paradigm. However, research in this area has to-date not considered who generates the data; symptoms reported by a woman versus a man, for example, could also have different implications. We propose a novel population-aware domain adaptation approach by formulating the domain adaptation task as a multi-source hierarchical Bayesian framework. The model improves prediction in the case of largely unlabelled target data by harnessing both domain and population invariant information. Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1811.08579 Zobrazit plný text záznamu View this record from Arxiv