Eric B. Laber

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NC State University

A spatiotemporal recommendation engine for malaria control

Malaria is an infectious disease affecting a large population across the...
03/11/2020 ∙ by Qian Guan, et al. ∙ 0 ∙ share

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High dimensional precision medicine from patient-derived xenografts

The complexity of human cancer often results in significant heterogeneit...
12/13/2019 ∙ by Naim U. Rashid, et al. ∙ 0 ∙ share

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Sample Size Calculations for SMARTs

Sequential Multiple Assignment Randomized Trials (SMARTs) are considered...
06/16/2019 ∙ by Eric J. Rose, et al. ∙ 0 ∙ share

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Global forensic geolocation with deep neural networks

An important problem in forensic analyses is identifying the provenance ...
05/28/2019 ∙ by Neal S. Grantham, et al. ∙ 0 ∙ share

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Note on Thompson sampling for large decision problems

There is increasing interest in using streaming data to inform decision ...
05/12/2019 ∙ by Tao Hu, et al. ∙ 0 ∙ share

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Efficient augmentation and relaxation learning for individualized treatment rules using observational data

Individualized treatment rules aim to identify if, when, which, and to w...
01/03/2019 ∙ by Ying-Qi Zhao, et al. ∙ 0 ∙ share

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Generalization error for decision problems

In this entry we review the generalization error for classification and ...
12/20/2018 ∙ by Eric B. Laber, et al. ∙ 0 ∙ share

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Hierarchical Continuous Time Hidden Markov Model, with Application in Zero-Inflated Accelerometer Data

Wearable devices including accelerometers are increasingly being used to...
12/04/2018 ∙ by Zekun Xu, et al. ∙ 0 ∙ share

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Thompson Sampling for Pursuit-Evasion Problems

Pursuit-evasion is a multi-agent sequential decision problem wherein a g...
11/11/2018 ∙ by Zhen Li, et al. ∙ 0 ∙ share

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Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals

Tooth loss from periodontal disease is a major public health burden in t...
10/10/2018 ∙ by Qian Guan, et al. ∙ 0 ∙ share

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Receiver Operating Characteristic Curves and Confidence Bands for Support Vector Machines

Many problems that appear in biomedical decision making, such as diagnos...
07/17/2018 ∙ by Daniel J. Luckett, et al. ∙ 0 ∙ share

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Estimation and Optimization of Composite Outcomes

There is tremendous interest in precision medicine as a means to improve...
11/28/2017 ∙ by Daniel J. Luckett, et al. ∙ 0 ∙ share

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Sufficient Markov Decision Processes with Alternating Deep Neural Networks

Advances in mobile computing technologies have made it possible to monit...
04/25/2017 ∙ by Longshaokan Wang, et al. ∙ 0 ∙ share

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Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning

The vision for precision medicine is to use individual patient character...
11/10/2016 ∙ by Daniel J. Luckett, et al. ∙ 0 ∙ share

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Set-valued dynamic treatment regimes for competing outcomes

Dynamic treatment regimes operationalize the clinical decision process a...
07/12/2012 ∙ by Eric B. Laber, et al. ∙ 0 ∙ share

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Q- and A-Learning Methods for Estimating Optimal Dynamic Treatment Regimes

In clinical practice, physicians make a series of treatment decisions ov...
02/19/2012 ∙ by Phillip J. Schulte, et al. ∙ 0 ∙ share

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Eric B. Laber

Featured Co-authors

Zhen Li

Brian J. Reich

Yang Ning

Michael R. Kosorok

Tao Hu

Yufeng Liu

Ana-Maria Staicu

Dipankar Bandyopadhyay

Zekun Xu

Longshaokan Wang

Daniel J. Lizotte

A spatiotemporal recommendation engine for malaria control

High dimensional precision medicine from patient-derived xenografts

Sample Size Calculations for SMARTs

Global forensic geolocation with deep neural networks

Note on Thompson sampling for large decision problems

Efficient augmentation and relaxation learning for individualized treatment rules using observational data

Generalization error for decision problems

Hierarchical Continuous Time Hidden Markov Model, with Application in Zero-Inflated Accelerometer Data

Thompson Sampling for Pursuit-Evasion Problems

Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals

Receiver Operating Characteristic Curves and Confidence Bands for Support Vector Machines

Estimation and Optimization of Composite Outcomes

Sufficient Markov Decision Processes with Alternating Deep Neural Networks

Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning

Set-valued dynamic treatment regimes for competing outcomes

Q- and A-Learning Methods for Estimating Optimal Dynamic Treatment Regimes