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Useful Interpretability for Real-World Machine Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Useful Interpretability for Real-World Machine Learning./
Author:	Singh, Chandan.
Description:	1 online resource (167 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
Contained By:	Dissertations Abstracts International84-03B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29208255click for full text (PQDT)
ISBN:	9798351476193

Useful Interpretability for Real-World Machine Learning.
Singh, Chandan.

Useful Interpretability for Real-World Machine Learning. - 1 online resource (167 pages)

Source: Dissertations Abstracts International, Volume: 84-03, Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2022.

Includes bibliographical references

The recent surge in highly successful, but opaque, machine-learning models has given rise to a dire need for interpretability. This work addresses the problem of interpretability with novel definitions, methodology, and scientific investigations, ensuring that interpretations are useful by grounding them in the context of real-world problems and audiences. We begin by defining what we mean by interpretability and some desiderata surrounding it, emphasizing the underappreciated role of context. We then dive into novel methods for interpreting/improving neural network models, focusing on how to best score, use, and distill interactions. Next, we turn from neural networks to relatively simple rule-based models, where we investigate how to improve predictive performance while maintaining an extremely concise model. Finally, we conclude with work on open-source software and data for facilitating interpretable data science. In each case, we dive into a specific context which motivates the proposed methodology, ranging from cosmology to cell biology to medicine. Code for everything is available at github.com/csinva.

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

Mode of access: World Wide Web

ISBN: 9798351476193Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Deep learningIndex Terms--Genre/Form:

542853
Electronic books.

Useful Interpretability for Real-World Machine Learning.
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245 1 0 $a Useful Interpretability for Real-World Machine Learning.
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300 $a 1 online resource (167 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
500 $a Advisor: Yu, Bin.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2022.
504 $a Includes bibliographical references
520 $a The recent surge in highly successful, but opaque, machine-learning models has given rise to a dire need for interpretability. This work addresses the problem of interpretability with novel definitions, methodology, and scientific investigations, ensuring that interpretations are useful by grounding them in the context of real-world problems and audiences. We begin by defining what we mean by interpretability and some desiderata surrounding it, emphasizing the underappreciated role of context. We then dive into novel methods for interpreting/improving neural network models, focusing on how to best score, use, and distill interactions. Next, we turn from neural networks to relatively simple rule-based models, where we investigate how to improve predictive performance while maintaining an extremely concise model. Finally, we conclude with work on open-source software and data for facilitating interpretable data science. In each case, we dive into a specific context which motivates the proposed methodology, ranging from cosmology to cell biology to medicine. Code for everything is available at github.com/csinva.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 523869
653 $a Deep learning
653 $a Explainability
653 $a Interpretability
653 $a Regularization
653 $a Tree
653 $a Wavelet
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 783688
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773 0 $t Dissertations Abstracts International $g 84-03B.
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