{"total":36,"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":"2306.09468","title":"FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods","created":"2023-06-15","cited_by_count":33,"area":null},{"id":"2310.19691","title":"Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness","created":"2023-10-30","cited_by_count":17,"area":null},{"id":"2210.07441","title":"Characterizing the Influence of Graph Elements","created":"2022-10-14","cited_by_count":16,"area":null},{"id":"2301.12951","title":"Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks","created":"2023-01-30","cited_by_count":16,"area":null},{"id":"2210.01953","title":"Robust Fair Clustering: A Novel Fairness Attack and Defense Framework","created":"2022-10-04","cited_by_count":13,"area":null},{"id":"2402.12789","title":"Fairness Without Harm: An Influence-Guided Active Sampling Approach","created":"2024-02-20","cited_by_count":12,"area":null},{"id":"2207.06084","title":"Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning","created":"2022-07-13","cited_by_count":10,"area":null},{"id":"2211.15897","title":"Learning Antidote Data to Individual Unfairness","created":"2022-11-29","cited_by_count":8,"area":null},{"id":"2206.00667","title":"How Biased are Your Features?: Computing Fairness Influence Functions with Global Sensitivity Analysis","created":"2022-06-01","cited_by_count":7,"area":null},{"id":"2303.01928","title":"Towards Algorithmic Fairness by means of Instance-level Data Re-weighting based on Shapley Values","created":"2023-03-03","cited_by_count":7,"area":null},{"id":"2310.02533","title":"Quantifying and mitigating the impact of label errors on model disparity metrics","created":"2023-10-04","cited_by_count":7,"area":null},{"id":"2404.00712","title":"Survey of Computerized Adaptive Testing: A Machine Learning Perspective","created":"2024-03-31","cited_by_count":6,"area":null},{"id":"2503.07315","title":"Group-robust Sample Reweighting for Subpopulation Shifts via Influence Functions","created":"2025-03-10","cited_by_count":6,"area":null},{"id":"2405.17489","title":"On the Inflation of KNN-Shapley Value","created":"2024-05-25","cited_by_count":4,"area":null},{"id":"2506.06656","title":"Rescaled Influence Functions: Accurate Data Attribution in High Dimension","created":"2025-06-07","cited_by_count":4,"area":null},{"id":"2506.07861","title":"Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective","created":"2025-06-09","cited_by_count":3,"area":null},{"id":"2508.14648","title":"Understanding Data Influence with Differential Approximation","created":"2025-08-20","cited_by_count":3,"area":null},{"id":"2303.17566","title":"Non-Invasive Fairness in Learning through the Lens of Data Drift","created":"2023-03-30","cited_by_count":2,"area":null},{"id":"2409.05208","title":"Influence-based Attributions can be Manipulated","created":"2024-09-08","cited_by_count":2,"area":null},{"id":"2507.06931","title":"DICE: Data Influence Cascade in Decentralized Learning","created":"2025-07-09","cited_by_count":2,"area":null},{"id":"2210.13954","title":"I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data","created":"2022-10-25","cited_by_count":1,"area":null},{"id":"2307.11379","title":"Towards Better Fairness-Utility Trade-off: A Comprehensive Measurement-Based Reinforcement Learning Framework","created":"2023-07-21","cited_by_count":1,"area":null},{"id":"2309.08375","title":"Boosting Fair Classifier Generalization through Adaptive Priority Reweighing","created":"2023-09-15","cited_by_count":1,"area":null},{"id":"2309.08678","title":"Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions","created":"2023-09-15","cited_by_count":1,"area":null},{"id":"2309.14243","title":"Enhancing data efficiency in reinforcement learning: a novel imagination mechanism based on mesh information propagation","created":"2023-09-25","cited_by_count":1,"area":null},{"id":"2411.02467","title":"Towards Harmless Rawlsian Fairness Regardless of Demographic Prior","created":"2024-11-04","cited_by_count":1,"area":null},{"id":"2602.19130","title":"Detecting labeling bias using influence functions","created":"2026-02-22","cited_by_count":1,"area":null},{"id":"2306.17828","title":"On the Cause of Unfairness: A Training Sample Perspective","created":"2023-06-30","cited_by_count":0,"area":null},{"id":"2405.15240","title":"Towards Real-world Debiasing: Rethinking Evaluation, Challenge, and Solution","created":"2024-05-24","cited_by_count":0,"area":null},{"id":"2405.17627","title":"Salutary Labeling with Zero Human Annotation","created":"2024-05-27","cited_by_count":0,"area":null},{"id":"2502.11031","title":"A Critical Review of Predominant Bias in Neural Networks","created":"2025-02-16","cited_by_count":0,"area":null},{"id":"2504.14882","title":"Some Optimizers are More Equal: Understanding the Role of Optimizers in Group Fairness","created":"2025-04-21","cited_by_count":0,"area":null},{"id":"2506.03777","title":"FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning","created":"2025-06-04","cited_by_count":0,"area":null},{"id":"2512.07249","title":"IFFair: Influence Function-driven Sample Reweighting for Fair Classification","created":"2025-12-08","cited_by_count":0,"area":null},{"id":"2607.28946","title":"Shapley-Value-Based Feature Attribution for Data Masking","created":"2026-07-31","cited_by_count":0,"area":null}],"next_cursor":null}