Towards externally valid machine learning: A spurious correlations perspective
Salaudeen, Olawale Elijah
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https://hdl.handle.net/2142/125683
Description
Title
Towards externally valid machine learning: A spurious correlations perspective
Author(s)
Salaudeen, Olawale Elijah
Issue Date
2024-07-02
Director of Research (if dissertation) or Advisor (if thesis)
Koyejo, Oluwasanmi
Doctoral Committee Chair(s)
Koyejo, Oluwasanmi
Committee Member(s)
Forsyth, David
Zhao, Han
D'Amour, Alexander
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Domain Generalization
External Validity
Spurious Correlations
Machine Learning Benchmarks
Abstract
As machine learning models permeate critical real-world systems, their reliability becomes paramount. However, their performance often critically depends on how representative their training data is of the deployment domain. This can lead to significant challenges when faced with distribution shifts—situations where the real-world data diverges from the training data. A significant issue under distribution shifts is the presence of spurious correlations—statistical associations in the training data that do not hold in all real-world domains. This dissertation develops methodologies that leverage causality to create machine learning models without spurious correlations, yielding more robust, fair, and ethical decision-making. Causality is crucial because it helps disentangle true causal relationships from mere statistical associations, ensuring that models learn robust, externally valid reasoning. This dissertation also establishes construct validity conditions for evaluating machine learning models under distribution shifts, enabling reliable generalization of robustness from distribution shift benchmarks to the real world. Domain-general models and robust evaluation methods enable the development of externally valid machine learning systems.
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