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cs.LG2025
Fair Supervised Learning Through Constraints on Smooth Nonconvex Unfairness-Measure Surrogates
Zahra Khatti, Daniel P. Robinson, Frank E. Curtis
A new strategy for fair supervised machine learning is proposed. The main advantages of the proposed strategy as compared to others in the literature are as follows. (a) We introdu…
cs.LG2024
Using Synthetic Data to Mitigate Unfairness and Preserve Privacy in Collaborative Machine Learning
Chia-Yuan Wu, Frank E. Curtis, Daniel P. Robinson
In distributed computing environments, collaborative machine learning enables multiple clients to train a global model collaboratively. To preserve privacy in such settings, a comm…