Work

Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease.

Year: 2016

Type: article

Abstract: with the testing. We derive a simple algorithm and study its convergence and consistency properties in detail, and provide lower-bound strategies based on recent work in continuous optimization. On a dataset of individuals at risk for Alzheimer's disease, our framework is competitive with alternative procedures that are twice as expensive and in some cases operationally infeasible to implement.

Source: PubMed

Authors: Hao Henry Zhou, Sathya N Ravi, Vamsi K Ithapu, Sterling C Johnson, Grace Wahba, Vikas Singh

Institutions: University of Wisconsin–Madison, William S. Middleton Memorial Veterans Hospital

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institute on Aging, National Institute of Allergy and Infectious Diseases, National Center for Research Resources

Awards: Maximizing Power in AD Clinical Trials via Multimodal Machine Learning, Institutional Clinical and Translational Science Award (UW-Madison)(UL1), The Center for Predictive Computational Phenotyping-1 Overall, Wisconsin Alzheimer's Disease Research Center, The Longitudinal Course of Imaging Biomarkers in People At Risk for Alzheimer’s Disease, Wisconsin Registry for Alzheimer's Prevention

OpenAlex ID: W2557228046

FWCI: 2.141

Cites: 33

Cited by: 20

Related to: 10