Search Results for author: Eric B. Laber

Found 10 papers, 0 papers with code

Sequential Knockoffs for Variable Selection in Reinforcement Learning

no code implementations24 Mar 2023 Tao Ma, Hengrui Cai, Zhengling Qi, Chengchun Shi, Eric B. Laber

In real-world applications of reinforcement learning, it is often challenging to obtain a state representation that is parsimonious and satisfies the Markov property without prior knowledge.

reinforcement-learning Variable Selection

Global forensic geolocation with deep neural networks

no code implementations28 May 2019 Neal S. Grantham, Brian J. Reich, Eric B. Laber, Krishna Pacifici, Robert R. Dunn, Noah Fierer, Matthew Gebert, Julia S. Allwood, Seth A. Faith

An important problem in forensic analyses is identifying the provenance of materials at a crime scene, such as biological material on a piece of clothing.

Thompson Sampling for Pursuit-Evasion Problems

no code implementations11 Nov 2018 Zhen Li, Nicholas J. Meyer, Eric B. Laber, Robert Brigantic

We propose a variant of Thompson Sampling for pursuit-evasion that allows for the application of existing model-based planning algorithms.

Thompson Sampling

Receiver Operating Characteristic Curves and Confidence Bands for Support Vector Machines

no code implementations17 Jul 2018 Daniel J. Luckett, Eric B. Laber, Samer S. El-Kamary, Cheng Fan, Ravi Jhaveri, Charles M. Perou, Fatma M. Shebl, Michael R. Kosorok

We propose a method for constructing confidence bands for the SVM ROC curve and provide the theoretical justification for the SVM ROC curve by showing that the risk function of the estimated decision rule is uniformly consistent across the weight parameter.

Binary Classification Decision Making +1

Estimation and Optimization of Composite Outcomes

no code implementations28 Nov 2017 Daniel J. Luckett, Eric B. Laber, Michael R. Kosorok

Estimated composite outcomes are subsequently used to construct an estimator of an individualized treatment rule which maximizes the mean of patient-specific composite outcomes.

Sufficient Markov Decision Processes with Alternating Deep Neural Networks

no code implementations25 Apr 2017 Longshaokan Wang, Eric B. Laber, Katie Witkiewitz

Advances in mobile computing technologies have made it possible to monitor and apply data-driven interventions across complex systems in real time.

Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning

no code implementations10 Nov 2016 Daniel J. Luckett, Eric B. Laber, Anna R. Kahkoska, David M. Maahs, Elizabeth Mayer-Davis, Michael R. Kosorok

However, existing methods for estimating optimal dynamic treatment regimes are designed for a small number of fixed decision points occurring on a coarse time-scale.

Decision Making

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

no code implementations19 Feb 2012 Phillip J. Schulte, Anastasios A. Tsiatis, Eric B. Laber, Marie Davidian

In clinical practice, physicians make a series of treatment decisions over the course of a patient's disease based on his/her baseline and evolving characteristics.

Statistical Inference in Dynamic Treatment Regimes

no code implementations30 Jun 2010 Eric B. Laber, Min Qian, Dan J. Lizotte, William E. Pelham, Susan A. Murphy

We then review an interesting challenge, that of nonregularity that often arises in this area.

Decision Making

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