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University of Illinois at Urbana-Champaign

Reliable and efficient machine learning under distribution shifts

dc:creator Wang, Haoxiang

thesis:degree_name Ph.D.

dc:date2024

Abstract

dc:description

Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01

Degree

thesis:*

Name thesis:degree_name: Ph.D.
Level thesis:degree_level: Dissertation
Discipline thesis:degree_discipline: Electrical & Computer Engr
Grantor: University of Illinois at Urbana-Champaign
Year dc:date: 2024

Author and committee

dc:creator, dc:contributor.*

Author dc:creator: Wang, Haoxiang
Contributors dc:contributor: Zhao, Han
Li, Bo
Schwing, Alexander Gerhard
Raginsky, Maxim

Subjects

dc:subject × 3

Rights

dc:rights

Statement dc:rights: Copyright 2024 Haoxiang Wang
Language dc:language: en, eng

Identifiers

dc:identifier.*

Handle dc:identifier: https://hdl.handle.net/2142/127492
OAI identifier oai:identifier: oai:www.ideals.illinois.edu:2142/127492

Chain of custody

source

Harvested from: University of Illinois - Urbana-Champaign
Base URL: www.ideals.illinois.edu/oai-pmh
Last updated: 2026-07-22
Source record: OAI-PMH GetRecord

related terms

citation

Wang, Haoxiang. Reliable and efficient machine learning under distribution shifts. Dissertation thesis, University of Illinois at Urbana-Champaign, 2024. https://hdl.handle.net/2142/127492

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