{"total":23,"rows":[{"id":"2207.07068","title":"Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey","created":"2022-07-14","cited_by_count":125,"area":null},{"id":"2211.01528","title":"Fair and Optimal Classification via Post-Processing","created":"2022-11-03","cited_by_count":51,"area":null},{"id":"2209.00427","title":"Fair learning with Wasserstein barycenters for non-decomposable performance measures","created":"2022-09-01","cited_by_count":24,"area":null},{"id":"2401.07187","title":"A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models","created":"2024-01-14","cited_by_count":20,"area":null},{"id":"2301.11781","title":"Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions","created":"2023-01-27","cited_by_count":19,"area":null},{"id":"2205.07182","title":"Fair Bayes-Optimal Classifiers Under Predictive Parity","created":"2022-05-15","cited_by_count":16,"area":null},{"id":"2310.05725","title":"Post-hoc Bias Scoring Is Optimal For Fair Classification","created":"2023-10-09","cited_by_count":14,"area":null},{"id":"2405.07393","title":"Intrinsic Fairness-Accuracy Tradeoffs under Equalized Odds","created":"2024-05-12","cited_by_count":7,"area":null},{"id":"2211.15072","title":"FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee","created":"2022-11-28","cited_by_count":5,"area":null},{"id":"2312.10396","title":"How Far Can Fairness Constraints Help Recover From Biased Data?","created":"2023-12-16","cited_by_count":5,"area":null},{"id":"2407.12710","title":"A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems","created":"2024-07-17","cited_by_count":5,"area":null},{"id":"2405.17782","title":"Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing","created":"2024-05-28","cited_by_count":4,"area":null},{"id":"2303.17713","title":"Mitigating Source Bias for Fairer Weak Supervision","created":"2023-03-30","cited_by_count":3,"area":null},{"id":"2402.09560","title":"Distribution-Free Rates in Neyman-Pearson Classification","created":"2024-02-14","cited_by_count":3,"area":null},{"id":"2311.05866","title":"Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes","created":"2023-11-10","cited_by_count":2,"area":null},{"id":"2409.00553","title":"Multi-Output Distributional Fairness via Post-Processing","created":"2024-08-31","cited_by_count":2,"area":null},{"id":"2409.02471","title":"Demographic parity in regression and classification within the unawareness framework","created":"2024-09-04","cited_by_count":2,"area":null},{"id":"2601.23221","title":"Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints","created":"2026-01-30","cited_by_count":2,"area":null},{"id":"2302.05906","title":"On Comparing Fair Classifiers under Data Bias","created":"2023-02-12","cited_by_count":1,"area":null},{"id":"2406.03142","title":"On the Power of Randomization in Fair Classification and Representation","created":"2024-06-05","cited_by_count":1,"area":null},{"id":"2402.16158","title":"Distribution-Free Fair Federated Learning with Small Samples","created":"2024-02-25","cited_by_count":0,"area":null},{"id":"2511.00359","title":"Toward Unifying Group Fairness Evaluation from a Sparsity Perspective","created":"2025-11-01","cited_by_count":0,"area":null},{"id":"2606.28097","title":"Fair Classification with Efficient and Post-hoc Controllable Fairness-Accuracy Trade-off","created":"2026-06-26","cited_by_count":0,"area":null}],"next_cursor":null}