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Reliability and Stability in Statistical and Machine Learning Problems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Reliability and Stability in Statistical and Machine Learning Problems./
作者:	Gupta, Suyash.
面頁冊數:	1 online resource (210 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-04, Section: B.
Contained By:	Dissertations Abstracts International84-04B.
標題:	Confidence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29342322click for full text (PQDT)
ISBN:	9798351499253

Reliability and Stability in Statistical and Machine Learning Problems.
Gupta, Suyash.

Reliability and Stability in Statistical and Machine Learning Problems. - 1 online resource (210 pages)

Source: Dissertations Abstracts International, Volume: 84-04, Section: B.

Thesis (Ph.D.)--Stanford University, 2022.

Includes bibliographical references

The increasing application of statistical machine learning techniques has substantial promise in diverse fields such as medicine, policy-making, and finance. Hence, very complex machine learning models are being built for use in automated decision-making protocols. However, with the growing sophistication of these models, they are often impossible to analyze statistically, and hence, the predictions made by such models often come with no guarantees. Furthermore, we often have data from heterogeneous sources, shifting distributions, or only have access to noisy or weakly supervised data (e.g., it may be expensive to obtain properly labeled data for a classification task). With such additional challenges, how can we trust the results we achieve when we apply machine-learned methods particularly in critical areas? Further, a shift in data generating distributions makes it challenging for statistical knowledge to generalize well. For instance, the performance of predictive models may drastically deteriorate when deployed on a new test set or a statistical finding from an experiment may not replicate in future experiments. Is it possible to identify situations where models or statistical findings are sensitive to changes in the data generating distributions? Can we suggest methods to transfer statistical knowledge more accurately in such situations? To address the above challenges, this dissertation focuses on developing methods that leverage conformal inference, distributional robustness, and causal inference. The ecacy of these methods are further demonstrated via extensive experiments including applications to real-world machine learning and statistical problems.

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

Mode of access: World Wide Web

ISBN: 9798351499253Subjects--Topical Terms:

682645
Confidence.
Index Terms--Genre/Form:

542853
Electronic books.

Reliability and Stability in Statistical and Machine Learning Problems.
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520 $a The increasing application of statistical machine learning techniques has substantial promise in diverse fields such as medicine, policy-making, and finance. Hence, very complex machine learning models are being built for use in automated decision-making protocols. However, with the growing sophistication of these models, they are often impossible to analyze statistically, and hence, the predictions made by such models often come with no guarantees. Furthermore, we often have data from heterogeneous sources, shifting distributions, or only have access to noisy or weakly supervised data (e.g., it may be expensive to obtain properly labeled data for a classification task). With such additional challenges, how can we trust the results we achieve when we apply machine-learned methods particularly in critical areas? Further, a shift in data generating distributions makes it challenging for statistical knowledge to generalize well. For instance, the performance of predictive models may drastically deteriorate when deployed on a new test set or a statistical finding from an experiment may not replicate in future experiments. Is it possible to identify situations where models or statistical findings are sensitive to changes in the data generating distributions? Can we suggest methods to transfer statistical knowledge more accurately in such situations? To address the above challenges, this dissertation focuses on developing methods that leverage conformal inference, distributional robustness, and causal inference. The ecacy of these methods are further demonstrated via extensive experiments including applications to real-world machine learning and statistical problems.
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