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Embracing Data-Centric AI : = Practical and Provable Solutions to Weakly Supervised Data.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Embracing Data-Centric AI :/
Reminder of title:	Practical and Provable Solutions to Weakly Supervised Data.
Author:	Zhu, Zhaowei.
Description:	1 online resource (302 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798380345132

Embracing Data-Centric AI : = Practical and Provable Solutions to Weakly Supervised Data.
Zhu, Zhaowei.

Embracing Data-Centric AI :Practical and Provable Solutions to Weakly Supervised Data. - 1 online resource (302 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Thesis (Ph.D.)--University of California, Santa Cruz, 2023.

Includes bibliographical references

Machine learning is a garbage-in-garbage-out system, which relies on high-quality labeled data to train models. However, in real-world scenarios, data quality issues are prevalent, leading to poor model performance and undesirable outcomes. Weakly supervised learning approaches have emerged as a promising solution to address this issue, enabling artificial intelligence (AI) systems to learn from noisy or unlabeled data. In this dissertation, we delve into data-centric AI and provide practical and provable solutions for handling weakly supervised data. Particularly, we introduce a pipeline with three important procedures to handle the data issues in weakly-supervised learning, including 1) a data diagnosis algorithm that learns the noise rates when true labels are missing, 2) a data curation algorithm that detects and fixes the corrupted labels, and 3) robust learning algorithms with the curated data. Moreover, we also discuss a multi-dimensional evaluation of model performance beyond the accuracy when the data is imperfect. All the works mentioned above have been open-sourced. The data diagnosis and curation pipeline is available at https://github.com/Docta-ai/docta.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380345132Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Data diagnosis and curationIndex Terms--Genre/Form:

554714
Electronic books.

Embracing Data-Centric AI : = Practical and Provable Solutions to Weakly Supervised Data.
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