no code implementations • 25 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.
no code implementations • 28 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.
no code implementations • 10 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.
no code implementations • 19 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.
no code implementations • 30 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.
no code implementations • 17 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.
no code implementations • 11 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.
no code implementations • 28 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.
no code implementations • 13 Dec 2019 • Naim U. Rashid, Daniel J. Luckett, Jingxiang Chen, Michael T. Lawson, Longshaokan Wang, Yunshu Zhang, Eric B. Laber, Yufeng Liu, Jen Jen Yeh, Donglin Zeng, Michael R. Kosorok
PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments.
no code implementations • 24 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.