تقرير
Fairness, Accuracy, and Unreliable Data
| العنوان: | Fairness, Accuracy, and Unreliable Data |
|---|---|
| المؤلفون: | Stangl, Kevin |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | 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. Comment: PhD thesis |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2408.16040 |
| رقم الأكسشن: | edsarx.2408.16040 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |