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Found 3 papers, 1 papers with code
Forgetting Fast in Recommender Systems
In this paper, we investigate fast machine unlearning techniques for recommender systems that can remove the effect of a small amount of training data from the recommendation model without incurring the full cost of retraining.
Obtaining Dyadic Fairness by Optimal Transport
Fairness has been taken as a critical metric in machine learning models, which is considered as an important component of trustworthy machine learning.
Fair Differential Privacy Can Mitigate the Disparate Impact on Model Accuracy
This paper proposes a fair differential privacy algorithm (FairDP) to mitigate the disparate impact on model accuracy for each class.
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