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Interpretable Data Science : = Applications and Theory in Climate Science and Public Health.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Interpretable Data Science :/
其他題名:	Applications and Theory in Climate Science and Public Health.
作者:	Stevens, Abby.
面頁冊數:	1 online resource (165 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29163906click for full text (PQDT)
ISBN:	9798834014553

Interpretable Data Science : = Applications and Theory in Climate Science and Public Health.
Stevens, Abby.

Interpretable Data Science :Applications and Theory in Climate Science and Public Health. - 1 online resource (165 pages)

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--The University of Chicago, 2022.

Includes bibliographical references

New statistical and machine learning methods have led to important advances in image and natural language processing, genetics, digital advertising, and other fields where there is an abundance of high-quality digital data and strong market incentives for automating tasks. This thesis intentionally focuses on areas such as climate science and public health, as they have suffered in this space without the same scale of training data and private investment.Part 1 of this thesis focuses on applications in climate science, specifically forecasting precipitation at seasonal timescales, which is a challenge due to complex dependence structures and a short observational record. To address these challenges, we develop a regularization regression scheme using a graph-guided regularizer that simultaneously promotes sparsity and similar coefficients for highly correlated covariates. We propose a novel way of combining climate model simulations and observations by using large ensemble simulations from a climate model to construct this regularizer, highlighting the potential to combine optimally the space-time structure of predictor variables learned from climate models with new graph-based regularizers to improve seasonal prediction. In Part 2, we develop a fast and flexible method for estimating variable importance (VI) measures with large neural networks. Our VI measure of interest analyzes the difference in predictive power between a full model trained on all variables and a reduced model that excludes the variable(s) of interest, which can be expensive to compute. We replace the need for fully retraining a wide neural network to estimate the reduced model by a linearization initialized at the full model parameters. We provide inferential guarantees for our method and verify its performance on simulated and real data. Part 3 of this thesis describes the development of city-scale synthetic populations for use in an agent-based model (CityCOVID) that simulates the endogenous transmission of COVID-19 and measures the impact of public health interventions.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798834014553Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Interpretable machine learningIndex Terms--Genre/Form:

542853
Electronic books.

Interpretable Data Science : = Applications and Theory in Climate Science and Public Health.
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