Search Results for author: Michael R. Kosorok

Found 17 papers, 4 papers with code

A Flexible Framework for Incorporating Patient Preferences Into Q-Learning

no code implementations22 Jul 2023 Joshua P. Zitovsky, Leslie Wilson, Michael R. Kosorok

In real-world healthcare problems, there are often multiple competing outcomes of interest, such as treatment efficacy and side effect severity.


Revisiting Bellman Errors for Offline Model Selection

1 code implementation31 Jan 2023 Joshua P. Zitovsky, Daniel de Marchi, Rishabh Agarwal, Michael R. Kosorok

Offline model selection (OMS), that is, choosing the best policy from a set of many policies given only logged data, is crucial for applying offline RL in real-world settings.

Atari Games Model Selection +1

Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes

no code implementations18 Sep 2022 Zuyue Fu, Zhengling Qi, Zhaoran Wang, Zhuoran Yang, Yanxun Xu, Michael R. Kosorok

Due to the lack of online interaction with the environment, offline RL is facing the following two significant challenges: (i) the agent may be confounded by the unobserved state variables; (ii) the offline data collected a prior does not provide sufficient coverage for the environment.

Offline RL reinforcement-learning +1

Neural interval-censored Cox regression with feature selection

no code implementations14 Jun 2022 Carlos García Meixide, Marcos Matabuena, Michael R. Kosorok

The classical Cox model emerged in 1972 promoting breakthroughs in how patient prognosis is quantified using time-to-event analysis in biomedicine.

feature selection regression

Discussion of Multiscale Fisher's Independence Test for Multivariate Dependence

no code implementations26 Apr 2022 Duyeol Lee, Helal El-Zaatari, Michael R. Kosorok, Xinyi Li, Kai Zhang

In this comment, we would like to discuss a general framework unifying the MULTIFIT and other tests and compare it with the binary expansion randomized ensemble test (BERET hereafter) proposed by Lee et al. (In press).


Kernel Assisted Learning for Personalized Dose Finding

1 code implementation19 Jul 2020 Liangyu Zhu, Wenbin Lu, Michael R. Kosorok, Rui Song

In this article, we propose a kernel assisted learning method for estimating the optimal individualized dose rule.

Decision Making

Missing Data Imputation for Classification Problems

no code implementations25 Feb 2020 Arkopal Choudhury, Michael R. Kosorok

The distance is weighted by Mutual Information (MI) which is a measure of feature relevance between the features and the class label.

Classification General Classification +1

Estimating heterogeneous treatment effects with right-censored data via causal survival forests

2 code implementations27 Jan 2020 Yifan Cui, Michael R. Kosorok, Erik Sverdrup, Stefan Wager, Ruoqing Zhu

Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation.

Technical Background for "A Precision Medicine Approach to Develop and Internally Validate Optimal Exercise and Weight Loss Treatments for Overweight and Obese Adults with Knee Osteoarthritis"

1 code implementation27 Jan 2020 Xiaotong Jiang, Amanda E. Nelson, Rebecca J. Cleveland, Daniel P. Beavers, Todd A. Schwartz, Liubov Arbeeva, Carolina Alvarez, Leigh F. Callahan, Stephen Messier, Richard Loeser, Michael R. Kosorok

We provide additional statistical background for the methodology developed in the clinical analysis of knee osteoarthritis in "A Precision Medicine Approach to Develop and Internally Validate Optimal Exercise and Weight Loss Treatments for Overweight and Obese Adults with Knee Osteoarthritis" (Jiang et al. 2020).


The Binary Expansion Randomized Ensemble Test (BERET)

no code implementations8 Dec 2019 Duyeol Lee, Kai Zhang, Michael R. Kosorok

Recently, the binary expansion testing framework was introduced to test the independence of two continuous random variables by utilizing symmetry statistics that are complete sufficient statistics for dependence.


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.

Causal nearest neighbor rules for optimal treatment regimes

no code implementations22 Nov 2017 Xin Zhou, Michael R. Kosorok

In this work, we propose a causal $k$-nearest neighbor method to estimate the optimal treatment regime.

Causal Inference Variable Selection

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

Biclustering Via Sparse Clustering

no code implementations11 Jul 2014 Qian Liu, Guanhua Chen, Michael R. Kosorok, Eric Bair

This framework can be used to identify biclusters that differ with respect to the means of the features, the variance of the features, or more general differences.


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