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Trevor Hastie

Researcher at Stanford University

Publications -  428
Citations -  230646

Trevor Hastie is an academic researcher from Stanford University. The author has contributed to research in topics: Lasso (statistics) & Feature selection. The author has an hindex of 124, co-authored 412 publications receiving 202592 citations. Previous affiliations of Trevor Hastie include University of Waterloo & University of Toronto.

Papers
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Shark detection and classification with machine learning

TL;DR: In this article , the authors used transfer learning and convolutional neural networks (CNNs) to classify sharks from videos and images using a database of 53,345 images covering 219 species of sharks.
Journal ArticleDOI

Construction of longitudinal prediction targets using semisupervised learning.

TL;DR: This study explores the joint use of empirical model fitting, clinical insights, and cross-validation based on how well formulated targets are predicted by clinically relevant baseline characteristics (antecedent validators) to triangulate valid prediction targets.
Posted Content

Longitudinal data analysis using matrix completion.

TL;DR: An alternative elementary framework for analyzing longitudinal data is proposed, relying on matrix completion, that covers multivariate longitudinal data, regression and can be easily extended to other settings and enables discovering that subtypes of Cerebral Palsy exhibit different progression trends.
Posted ContentDOI

Significant Sparse Polygenic Risk Scores across 428 traits in UK Biobank

TL;DR: In this article, a systematic assessment of polygenic risk score (PRS) prediction across more than 1,600 traits using genetic and phenotype data in the UK Biobank is presented.
Posted ContentDOI

Temporal dynamics of the multi-omic response to endurance exercise training across tissues

David Amar, +182 more
- 22 Sep 2022 - 
TL;DR: Interpretation of systemic and tissue-specific molecular adaptations produced hypotheses to help describe the health benefits induced by exercise, including candidate mechanisms that link training adaptation to non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health, and tissue injury and recovery.