 FaiREE: Fair Classification with Finite-Sample and Distribution-Free GuaranteeInternational Conference on Learning Representations (ICLR), 2022 |
 FairRR: Pre-Processing for Group Fairness through Randomized ResponseInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024 |
 FLAC: Fairness-Aware Representation Learning by Suppressing
Attribute-Class AssociationsIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023 |
 Bias Mitigation for Machine Learning Classifiers: A Comprehensive SurveyACM Journal on Responsible Computing (JRC), 2022 |
 Group Meritocratic Fairness in Linear Contextual BanditsNeural Information Processing Systems (NeurIPS), 2022 |
 A Reduction to Binary Approach for Debiasing Multiclass DatasetsNeural Information Processing Systems (NeurIPS), 2022 |
 Fair Bayes-Optimal Classifiers Under Predictive ParityNeural Information Processing Systems (NeurIPS), 2022 |
 Uncovering the Source of Machine BiasSocial Science Research Network (SSRN), 2022 |
 Fair Decision-Making for Food InspectionsConference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), 2021 |
 Fairness in Ranking under UncertaintyNeural Information Processing Systems (NeurIPS), 2021 |
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 A Near-Optimal Algorithm for Debiasing Trained Machine Learning ModelsNeural Information Processing Systems (NeurIPS), 2021 |
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 Fairness in Machine Learning: A SurveyACM Computing Surveys (ACM CSUR), 2020 |
 Fair Regression with Wasserstein BarycentersNeural Information Processing Systems (NeurIPS), 2020 |
 Leveraging Semi-Supervised Learning for Fairness using Neural NetworksInternational Conference on Machine Learning and Applications (ICMLA), 2019 |
 Inherent Tradeoffs in Learning Fair RepresentationsNeural Information Processing Systems (NeurIPS), 2019 |
 Leveraging Labeled and Unlabeled Data for Consistent Fair Binary
ClassificationNeural Information Processing Systems (NeurIPS), 2019 |
 Noise-tolerant fair classificationNeural Information Processing Systems (NeurIPS), 2019 |
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