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Toward a better trade-off between performance and fairness with kernel-based distribution matching.

, , , , , and . CoRR, (2019)

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Fairness without Demographics through Adversarially Reweighted Learning., , , , , , , and . NeurIPS, (2020)Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification., , , , , , , and . CoRR, (2023)Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning., , , , , and . KDD, page 1748-1757. ACM, (2021)Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective., , , , , , , , , and . AIES, page 873-883. ACM, (2021)Measuring Recommender System Effects with Simulated Users., , , , , , , , and . CoRR, (2021)Debiasing Embeddings for Reduced Gender Bias in Text Classification., , and . CoRR, (2019)Toward a better trade-off between performance and fairness with kernel-based distribution matching., , , , , and . CoRR, (2019)FRAPPÉ: A Post-Processing Framework for Group Fairness Regularization., , , , , and . CoRR, (2023)Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations., , , , , , , , , and 2 other author(s). CoRR, (2022)Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems., , , , , , and . WSDM, page 436-444. ACM, (2021)