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Enhancing Machine Learning Accuracy and Statistical Inference via Deep Generative Models /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Enhancing Machine Learning Accuracy and Statistical Inference via Deep Generative Models /Yifei Liu.
作者:	Liu, Yifei,
面頁冊數:	1 electronic resource (140 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
附註:	Advisors: Shen, Xiaotong Committee members: Ding, Jie; Pan, Wei; Rothman, Adam.
Contained By:	Dissertations Abstracts International86-03B.
標題:	Statistics.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31558659
ISBN:	9798384095682

Enhancing Machine Learning Accuracy and Statistical Inference via Deep Generative Models /
Liu, Yifei,

Enhancing Machine Learning Accuracy and Statistical Inference via Deep Generative Models /Yifei Liu. - 1 electronic resource (140 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Synthetic data refers to data generated by a mechanism designed to mimic the distribution of the raw data. In the era of generative artificial intelligence, the significance of synthetic data has dramatically increased. It offers numerous advantages in data science and machine learning tasks. For instance, synthetic data can be used to augment original datasets, helping to alleviate data scarcity and potentially enhancing the performance of predictive models. Synthetic data can also be tailored to meet standard privacy criteria, enabling data sharing and collaboration across different parties and platforms. For a systematic evaluation of synthetic data applied to downstream tasks, this thesis studies the "generation effect" --- how errors from generative models affect the accuracy/power of the downstream analysis. We provide practical and valid methods of utilizing synthetic data for both prediction and inference tasks, supported by both theoretical insights as well as numerical experiments.

English

ISBN: 9798384095682Subjects--Topical Terms:

182057
Statistics.
Subjects--Index Terms:

Deep learning

Enhancing Machine Learning Accuracy and Statistical Inference via Deep Generative Models /
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520 $a Synthetic data refers to data generated by a mechanism designed to mimic the distribution of the raw data. In the era of generative artificial intelligence, the significance of synthetic data has dramatically increased. It offers numerous advantages in data science and machine learning tasks. For instance, synthetic data can be used to augment original datasets, helping to alleviate data scarcity and potentially enhancing the performance of predictive models. Synthetic data can also be tailored to meet standard privacy criteria, enabling data sharing and collaboration across different parties and platforms. For a systematic evaluation of synthetic data applied to downstream tasks, this thesis studies the "generation effect" --- how errors from generative models affect the accuracy/power of the downstream analysis. We provide practical and valid methods of utilizing synthetic data for both prediction and inference tasks, supported by both theoretical insights as well as numerical experiments.
546 $a English
590 $a School code: 0130
650 4 $a Statistics. $3 182057
650 4 $a Computer engineering. $3 212944
650 4 $a Computer science. $3 199325
653 $a Deep learning
653 $a Generative models
653 $a Machine learning
653 $a Personalization
653 $a Privacy
653 $a Statistical inference
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690 $a 0984
690 $a 0464
710 2 $a University of Minnesota. $b Statistics. $e degree granting institution. $3 990189
720 1 $a Shen, Xiaotong $e degree supervisor.
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