Fairness, Accuracy, and Unreliable Data
This thesis investigates three areas targeted at improving the reliability of machine learning; fairness in machine learning, strategic classification, and algorithmic robustness. Each of these domains has special properties or structure that can complicate learning. A theme throughout this thesis is thinking about ways in which a `plain' empirical risk minimization algorithm will be misleading or ineffective because of a mis-match between classical learning theory assumptions and specific properties of some data distribution in the wild. Theoretical understanding in eachof these domains can help guide best practices and allow for the design of effective, reliable, and robust systems.
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FairnessLearning TheorySimilar Papers 제목 키워드 기반
Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning
Collaborative machine learning (CML) enables multiple clients to train a global model jointly in a data-distributed setting. To address data privacy and communication efficiency, one-shot CML has been increasingly adopte…
Bilevel OptimizationFLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data
Fairness-aware learning aims at constructing classifiers that not only make accurate predictions, but also do not discriminate against specific groups. It is a fast-growing area of machine learning with far-reaching soci…
FairnessOn The Impact of Machine Learning Randomness on Group Fairness
Statistical measures for group fairness in machine learning reflect the gap in performance of algorithms across different groups. These measures, however, exhibit a high variance between different training instances, whi…
FairnessLearning-Augmented Online Allocation under Unreliable Advice: Robustness, Exposure Fairness, and Distribution Shift
Learning-augmented algorithms improve online decisions using predictions, but unreliable advice may harm efficiency and fairness. We study an online allocation problem with finite candidate sets, irreversible decisions, …
On the Fairness, Diversity and Reliability of Text-to-Image Generative Models
The widespread availability of multimodal generative models has sparked critical discussions on their fairness, reliability, and potential for misuse. While text-to-image models can produce high-fidelity, user-guided ima…
DiversityEthicsFairness