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Reliable and Adaptive Stochastic Optimization in the Face of Messy Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Reliable and Adaptive Stochastic Optimization in the Face of Messy Data./
作者:	Xie, Miaolan.
面頁冊數:	1 online resource (151 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: A.
Contained By:	Dissertations Abstracts International85-12A.
標題:	Information science. -
電子資源:	click for full text (PQDT)
ISBN:	9798382842257

Reliable and Adaptive Stochastic Optimization in the Face of Messy Data.
Xie, Miaolan.

Reliable and Adaptive Stochastic Optimization in the Face of Messy Data. - 1 online resource (151 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: A.

Thesis (Ph.D.)--Cornell University, 2024.

Includes bibliographical references

Solving real-world stochastic optimization problems (e.g., in machine learning) presents two key challenges: the messiness of real-world data, which can be noisy, biased, or corrupted due to factors like outliers, distribution shifts, and even adversarial attacks; and the laborious, time-intensive requirement of manually tuning step sizes in many existing algorithms.I study stochastic adaptive optimization algorithms under a simple, common framework. The algorithms in this framework avoid the need for manual step size tuning by adaptively adjusting it in each iteration based on the algorithm's progress. To address the issue of messy data, the framework only assumes access to function-related information through probabilistic oracles, which may be biased and corrupted. This framework is very general, encompassing a wide range of algorithms, and is applicable to multiple problem settings, such as expected loss minimization in machine learning, simulation optimization, and derivative-free optimization. We establish iteration complexity bounds for two algorithms within it - stochastic adaptive step search and stochastic adaptive cubic-regularized Newton method - under reasonable oracle conditions. Additionally, we derive a meta-theorem to bound the sample complexity for any algorithm in the framework.

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

Mode of access: World Wide Web

ISBN: 9798382842257Subjects--Topical Terms:

561178
Information science.
Subjects--Index Terms:

Mathematical optimizationIndex Terms--Genre/Form:

554714
Electronic books.

Reliable and Adaptive Stochastic Optimization in the Face of Messy Data.
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520 $a Solving real-world stochastic optimization problems (e.g., in machine learning) presents two key challenges: the messiness of real-world data, which can be noisy, biased, or corrupted due to factors like outliers, distribution shifts, and even adversarial attacks; and the laborious, time-intensive requirement of manually tuning step sizes in many existing algorithms.I study stochastic adaptive optimization algorithms under a simple, common framework. The algorithms in this framework avoid the need for manual step size tuning by adaptively adjusting it in each iteration based on the algorithm's progress. To address the issue of messy data, the framework only assumes access to function-related information through probabilistic oracles, which may be biased and corrupted. This framework is very general, encompassing a wide range of algorithms, and is applicable to multiple problem settings, such as expected loss minimization in machine learning, simulation optimization, and derivative-free optimization. We establish iteration complexity bounds for two algorithms within it - stochastic adaptive step search and stochastic adaptive cubic-regularized Newton method - under reasonable oracle conditions. Additionally, we derive a meta-theorem to bound the sample complexity for any algorithm in the framework.
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