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Found 10 papers, 0 papers with code
Sequential Knockoffs for Variable Selection in Reinforcement Learning
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.
High dimensional precision medicine from patient-derived xenografts
PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments.
Global forensic geolocation with deep neural networks
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
We propose a variant of Thompson Sampling for pursuit-evasion that allows for the application of existing model-based planning algorithms.
Receiver Operating Characteristic Curves and Confidence Bands for Support Vector Machines
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.
Estimation and Optimization of Composite Outcomes
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
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
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.
$Q$- and $A$-Learning Methods for Estimating Optimal Dynamic Treatment Regimes
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
We then review an interesting challenge, that of nonregularity that often arises in this area.
