التفاصيل البيبلوغرافية
| العنوان: |
Fairness in Streaming Submodular Maximization over a Matroid Constraint |
| المؤلفون: |
El Halabi M., Fusco F., Norouzi-Fard A., Tardos J., Tarnawski J. |
| المساهمون: |
El Halabi, M., Fusco, F., Norouzi-Fard, A., Tardos, J., Tarnawski, J. |
| بيانات النشر: |
ML Research Press |
| سنة النشر: |
2023 |
| المجموعة: |
Sapienza Università di Roma: CINECA IRIS |
| مصطلحات موضوعية: |
submodular maximization, fairness |
| الوصف: |
Streaming submodular maximization is a natural model for the task of selecting a representative subset from a large-scale dataset. If datapoints have sensitive attributes such as gender or race, it becomes important to enforce fairness to avoid bias and discrimination. This has spurred significant interest in developing fair machine learning algorithms. Recently, such algorithms have been developed for monotone submodular maximization under a cardinality constraint. In this paper, we study the natural generalization of this problem to a matroid constraint. We give streaming algorithms as well as impossibility results that provide trade-offs between efficiency, quality and fairness. We validate our findings empirically on a range of well-known real-world applications: exemplar-based clustering, movie recommendation, and maximum coverage in social networks. |
| نوع الوثيقة: |
conference object |
| اللغة: |
English |
| Relation: |
ispartofbook:Proceedings of the 40 th International Conference on Machine Learning; International Conference on Machine Learning; volume:202; firstpage:9150; lastpage:9171; numberofpages:22; serie:PROCEEDINGS OF MACHINE LEARNING RESEARCH; https://hdl.handle.net/11573/1693543 |
| الاتاحة: |
https://hdl.handle.net/11573/1693543 |
| Rights: |
info:eu-repo/semantics/openAccess |
| رقم الانضمام: |
edsbas.F0DEBBF7 |
| قاعدة البيانات: |
BASE |