Using Differentiable Programming for Flexible Statistical Modeling.
Differentiable programming has recently received much interest as a paradigm that facilitates taking gradients of computer programs. While the corresponding flexible gradient-based optimization approaches so far have been used predominantly for deep learning or enriching the latter with modeling com...
| Publicado en: | American Statistician Vol. 76; no. 3; pp. 270 - 280 |
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| Autores principales: | , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Aug2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=158065777&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 158065777 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: Aug2022 vid: 76 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 158065777 10.1080/00031305.2021.2002189 ppf: 270 ppct: 10 formats: tig: atl: Using Differentiable Programming for Flexible Statistical Modeling. aug: au: Hackenberg, Maren Grodd, Marlon Kreutz, Clemens Fischer, Martina Esins, Janina Grabenhenrich, Linus Karagiannidis, Christian Binder, Harald affil: Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Germany Robert-Koch-Institut, Berlin, Germany Department of Pneumology and Critical Care Medicine, Cologne-Merheim Hospital, ARDS and ECMO Center, Kliniken der Stadt Köln, Witten/Herdecke University Hospital, Cologne, Germany su: Time pressure Statistical models Deep learning Delay differential equations Automatic differentiation Computer software sug: subj: Time pressure Software publishers (except video game publishers) Computer and Computer Peripheral Equipment and Software Merchant Wholesalers Computer, computer peripheral and pre-packaged software merchant wholesalers Computer and software stores Statistical models Deep learning Delay differential equations Automatic differentiation Computer software keyword: Differential equations Machine learning Optimization Workflow Differential equations Machine learning Optimization Workflow ab: Differentiable programming has recently received much interest as a paradigm that facilitates taking gradients of computer programs. While the corresponding flexible gradient-based optimization approaches so far have been used predominantly for deep learning or enriching the latter with modeling components, we want to demonstrate that they can also be useful for statistical modeling per se, for example, for quick prototyping when classical maximum likelihood approaches are challenging or not feasible. In an application from a COVID-19 setting, we use differentiable programming to quickly build and optimize a flexible prediction model adapted to the data quality challenges at hand. Specifically, we develop a regression model, inspired by delay differential equations, that can bridge temporal gaps of observations in the central German registry of COVID-19 intensive care cases for predicting future demand. With this exemplary modeling challenge, we illustrate how differentiable programming can enable simple gradient-based optimization of the model by automatic differentiation. This allowed us to quickly prototype a model under time pressure that outperforms simpler benchmark models. We thus exemplify the potential of differentiable programming also outside deep learning applications to provide more options for flexible applied statistical modeling. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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