Filters








15,858 Hits in 7.9 sec

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

Ying-Qi Zhao and Eric B. Laber and Yang Ning and Sumona Saha and Bruce Sands
2019 arXiv   pre-print
Using techniques from semiparametric efficiency theory, we derive rates of convergence for the proposed estimators and use these rates to characterize the bias-variance trade-off for estimating individualized  ...  Individualized treatment rules aim to identify if, when, which, and to whom treatment should be applied.  ...  Efficient augmentation and relaxation learning (EARL) Let M be the class of measurable functions from R p into R.  ... 
arXiv:1901.00663v1 fatcat:pe6yiwoxpfb7pjl5kblrsokzhy

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

Ying-Qi Zhao, Eric B Laber, Yang Ning, Sumona Saha, Bruce E Sands
2019 Journal of machine learning research  
Using techniques from semiparametric efficiency theory, we derive rates of convergence for the proposed estimators and use these rates to characterize the bias-variance trade-off for estimating individualized  ...  Individualized treatment rules aim to identify if, when, which, and to whom treatment should be applied.  ...  Acknowledgements This work was partially supported by R01DK108073, P01CA142538, R01DE024984, P30CA015704, S10 OD020069, DMS-1555141, and grants from the Crohn's and Colitis Foundation and the Centers for  ... 
pmid:31440118 pmcid:PMC6705615 fatcat:7hyryex5hvfrjhys2q27xgmas4

Constructing Stabilized Dynamic Treatment Regimes for Censored Data [article]

Ying-Qi Zhao and Ruoqing Zhu and Guanhua Chen and Yingye Zheng
2019 arXiv   pre-print
Stabilized dynamic treatment regimes are sequential decision rules for individual patients that not only adaptive throughout the disease progression but also remain consistent over time in format.  ...  The methods are further applied to the Framingham Study to derive treatment rules for cardiovascular disease.  ...  First, the number of stages for each individual in the study is not fixed. This is because the event time can vary by individual, and the treatment is usually stopped once the failure event happens.  ... 
arXiv:1808.01332v2 fatcat:evp5popdijhszgr66ekgqgwxbm

Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score [article]

Muxuan Liang, Young-Geun Choi, Yang Ning, Maureen A Smith, Ying-Qi Zhao
2021 arXiv   pre-print
In this work, we develop a penalized doubly robust method to estimate the optimal individualized treatment rule from high-dimensional data.  ...  , from large observational data.  ...  In this work, we propose a novel penalized doubly robust approach, termed as penalized efficient augmentation and relaxation learning, to estimate the optimal individualized treatment rule in observational  ... 
arXiv:2007.04445v3 fatcat:icmi5hctujdx5illid5723evsa

Stabilized Direct Learning for Efficient Estimation of Individualized Treatment Rules [article]

Kushal S. Shah, Haoda Fu, Michael R. Kosorok
2021 arXiv   pre-print
SD-Learning improves the efficiency of D-Learning estimates in binary and multi-arm treatment scenarios.  ...  Significant focus has been placed on creating algorithms to estimate individualized treatment rules (ITR), which map from patient covariates to the space of available treatments with the goal of maximizing  ...  ., and Sands, B. E. (2019). Efficient augmentation and relaxation learning for individualized treatment rules using observational data. Journal of Machine Learning Research 20, 48.  ... 
arXiv:2112.03981v1 fatcat:gbqye2ufwncqjgnwk2jg7exjbq

Subgroup Identification Using the personalized Package

Jared D. Huling, Menggang Yu
2021 Journal of Statistical Software  
Further estimation improvements can be obtained via efficiency augmentation.  ...  Keywords: subgroup identification, heterogeneity of treatment effect, interaction modeling, inverse weighting, individualized treatment rules, precision medicine.  ...  The views in this publication are solely the responsibility of the authors and do not necessarily represent the views of the PCORI, its Board of Governors or Methodology Committee.  ... 
doi:10.18637/jss.v098.i05 fatcat:vouhgcjtf5b4lbunf6sj5b7zea

Subgroup Identification Using the personalized Package [article]

Jared D. Huling, Menggang Yu
2018 arXiv   pre-print
Further estimation improvements can be obtained via efficiency augmentation.  ...  A plethora of disparate statistical methods have been proposed for subgroup identification to help tailor treatment decisions for patients.  ...  The views in this publication are solely the responsibility of the authors and do not necessarily represent the views of the PCORI, its Board of Governors or Methodology Committee.  ... 
arXiv:1809.07905v2 fatcat:g6apmf4vejbq5o2aih5tpvop3y

Robust Processing of Situated Spoken Dialogue [chapter]

Pierre Lison, Geert-Jan M. Kruijff
2009 Lecture Notes in Computer Science  
The parser takes word lattices as input and is able to handle ill-formed and misrecognised utterances by selectively relaxing its set of grammatical rules.  ...  The choice of the most relevant interpretation is then realised via a discriminative model augmented with contextual information.  ...  In order to handle disfluent, partial, ill-formed or misrecognized utterances, the grammar used by the parser is "relaxed" via the introduction of a set of non-standard rules which allow for the combination  ... 
doi:10.1007/978-3-642-04617-9_31 fatcat:x7hdjodgbnfefb3r2xvijwewqy

Spatial Capture-Recapture

Edward O. Garton
2014 Journal of Mammalogy  
sites added in a data augmentation analysis.  ...  : fixed covariates that are fully observed (e.g., dates of sampling occasions), partially observed individual covariates not known for all individuals (e.g., sex of animals), and unobserved covariates  ... 
doi:10.1644/14-mamm-r-096 fatcat:g5bp3tybsfdtxkou5kio3mc6lm

An Adjective Selection Personality Assessment Method Using Gradient Boosting Machine Learning

Bruno Fernandes, Alfonso González-Briones, Paulo Novais, Miguel Calafate, Cesar Analide, José Neves
2020 Processes  
Hence, a web platform was developed for data collection, requesting subjects to rate each adjective and select those describing him the most.  ...  Based on a Gradient Boosting approach, two distinct Machine Learning architectures were conceived, tuned and evaluated.  ...  Abbreviations The following abbreviations are used in this manuscript:  ... 
doi:10.3390/pr8050618 fatcat:7yciz6v3y5bmjojjxovaugkphe

Deep advantage learning for optimal dynamic treatment regime

Shuhan Liang, Wenbin Lu, Rui Song
2018 Statistical Theory and Related Fields  
The proposed deep A-learning methods are applied to a data from the STAR*D trial and are shown to have better performance compared with the penalized least square estimator using a linear decision rule  ...  However few research has been done on deep advantage learning (A-learning). In this paper, we present a deep A-learning approach to estimate optimal dynamic treatment regime.  ...  In computer vision, techniques like rotation and random cropping are widely used for data augmentation.  ... 
doi:10.1080/24754269.2018.1466096 pmid:30420972 pmcid:PMC6226036 fatcat:mmfasu47qbefhacee7o7sbqfau

Covariate Adjustment in Regression Discontinuity Designs [article]

Matias D. Cattaneo, Luke Keele, Rocio Titiunik
2021 arXiv   pre-print
The Regression Discontinuity (RD) design is a widely used non-experimental method for causal inference and program evaluation.  ...  In this chapter, we review the different roles of covariate adjustment in RD designs, and offer methodological guidance for its correct use in applications.  ...  is observed for a sample of treated individuals.  ... 
arXiv:2110.08410v1 fatcat:xfgn5u25czhwtc657ttcx7f77y

Improved Mild Closed Head Traumatic Brain Injury Outcomes With a Brain-Computer Interface Amplified Cognitive Remediation Training

Curtis T Cripe, Rebecca Cooper, Peter Mikulecky, Jason H Huang, Dallas C Hack
2021 Cureus  
the level of play required to properly engage long-term potentiation (LTP) and long-term depression (LTD) network learning rules.  ...  The mean percent change for the pooled trained domain was double that observed for the pooled untrained domain, at 17.2% versus 8.3%, respectively.  ...  Our use of data was retrospective, and data were processed for analysis in a manner that precluded the identification of individuals.  ... 
doi:10.7759/cureus.14996 pmid:34007777 pmcid:PMC8121126 fatcat:4sztciu5bfg57c5jfhvcyjewfm

Exploring the scope of neurometrically informed mechanism design

Ian Krajbich, Colin Camerer, Antonio Rangel
2017 Games and Economic Behavior  
We thank Phil Reny for his helpful comments on earlier drafts. Abstract.  ...  These mechanisms simultaneously satisfy efficiency, voluntary participation, and dominant strategy incentive compatibility, and are shown to elicit truth-telling behavior from subjects in two different  ...  as a function of treatment and experimental round.  ... 
doi:10.1016/j.geb.2016.05.001 fatcat:v3qujlu26nf3bjhfxmekttjva4

Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction [article]

Stephen Bach, Bert Huang, Ben London, Lise Getoor
2013 arXiv   pre-print
We introduce the first inference algorithm that is both scalable and applicable to the full class of HL-MRFs, and show how to train HL-MRFs with several learning algorithms.  ...  This paper demonstrates that HL-MRFs are general tools for fast and accurate structured prediction.  ...  Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation thereon.  ... 
arXiv:1309.6813v1 fatcat:7qs5govmtfcaxnjmtmzejtn5ju
« Previous Showing results 1 — 15 out of 15,858 results