Search Results - "Tay, J Kenneth"

Elastic Net Regularization Paths for All Generalized Linear Models by Tay, J Kenneth, Narasimhan, Balasubramanian, Hastie, Trevor

“…The lasso and elastic net are popular regularized regression models for supervised learning. Friedman, Hastie, and Tibshirani (2010) introduced a…”
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Feature-weighted elastic net: using "features of features" for better prediction by Tay, J Kenneth, Aghaeepour, Nima, Hastie, Trevor, Tibshirani, Robert

“…In some supervised learning settings, the practitioner might have additional information on the features used for prediction. We propose a new method which…”
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Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit by Shung, Dennis, Huang, Jessie, Castro, Egbert, Tay, J. Kenneth, Simonov, Michael, Laine, Loren, Batra, Ramesh, Krishnaswamy, Smita

“…Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit stay,…”
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Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding by Shung, Dennis L., Au, Benjamin, Taylor, Richard Andrew, Tay, J. Kenneth, Laursen, Stig B., Stanley, Adrian J., Dalton, Harry R., Ngu, Jeffrey, Schultz, Michael, Laine, Loren

“…Scoring systems are suboptimal for determining risk in patients with upper gastrointestinal bleeding (UGIB); these might be improved by a machine learning…”
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Reluctant Generalised Additive Modelling by Tay, J. Kenneth, Tibshirani, Robert

“…Summary Sparse generalised additive models (GAMs) are an extension of sparse generalised linear models that allow a model's prediction to vary non‐linearly…”
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Smooth Multi-Period Forecasting With Application to Prediction of COVID-19 Cases by Tuzhilina, Elena, Hastie, Trevor J., McDonald, Daniel J., Tay, J. Kenneth, Tibshirani, Robert

“…Forecasting methodologies have always attracted a lot of attention and have become an especially hot topic since the beginning of the COVID-19 pandemic. In…”
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Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules by Shung, Dennis, Tsay, Cynthia, Laine, Loren, Chang, David, Li, Fan, Thomas, Prem, Partridge, Caitlin, Simonov, Michael, Hsiao, Allen, Tay, J Kenneth, Taylor, Andrew

“…Background and Aim Guidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly…”
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Reluctant generalized additive modeling by Tay, J. Kenneth, Tibshirani, Robert

“…Sparse generalized additive models (GAMs) are an extension of sparse generalized linear models which allow a model's prediction to vary non-linearly with an…”
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Elastic Net Regularization Paths for All Generalized Linear Models by Tay, J. Kenneth, Narasimhan, Balasubramanian, Hastie, Trevor

“…The lasso and elastic net are popular regularized regression models for supervised learning. Friedman, Hastie, and Tibshirani (2010) introduced a…”
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A latent factor approach for prediction from multiple assays by Tay, J. Kenneth, Tibshirani, Robert

“…In many domains such as healthcare or finance, data often come in different assays or measurement modalities, with features in each assay having a common…”
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Principal component-guided sparse regression by Tay, J. Kenneth, Friedman, Jerome, Tibshirani, Robert

“…We propose a new method for supervised learning, especially suited to wide data where the number of features is much greater than the number of observations…”
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Smooth multi-period forecasting with application to prediction of COVID-19 cases by Tuzhilina, Elena, Hastie, Trevor J, McDonald, Daniel J, Tay, J. Kenneth, Tibshirani, Robert

“…Forecasting methodologies have always attracted a lot of attention and have become an especially hot topic since the beginning of the COVID-19 pandemic. In…”
Get full text

Feature-weighted elastic net: using "features of features" for better prediction by Tay, J. Kenneth, Aghaeepour, Nima, Hastie, Trevor, Tibshirani, Robert

“…In some supervised learning settings, the practitioner might have additional information on the features used for prediction. We propose a new method which…”
Get full text