fair machine learning

When do Minimax-fair Learning and Empirical Risk Minimization Coincide? ICML 2023 Counterfactual Fairness with Partially Known Causal Graph NeurIPS 2022 Fair Bayes-Optimal Classifiers Under Predictive Parity NeurIPS 2022 On Learning Fairness and Accuracy on Multiple Subgroups NeurIPS 2022 Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation NeurIPS 2022 Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness AAAI 2022 An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning NeurIPS 2021 Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem NeurIPS 2021 Protecting the Protected Group: Circumventing Harmful Fairness AAAI 2021 Retiring Adult: New Datasets for Fair Machine Learning NeurIPS 2021 Achieving Equalized Odds by Resampling Sensitive Attributes NeurIPS 2020 Fair Learning with Private Demographic Data ICML 2020 Fairness in Streaming Submodular Maximization: Algorithms and Hardness NeurIPS 2020 FR-Train: A Mutual Information-Based Approach to Fair and Robust Training ICML 2020 Incorporating Interpretable Output Constraints in Bayesian Neural Networks NeurIPS 2020 NestedVAE: Isolating Common Factors via Weak Supervision CVPR 2020 Rényi Fair Inference ICLR 2020 Fairness without Harm: Decoupled Classifiers with Preference Guarantees ICML 2019 The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric NeurIPS 2019 Blind Justice: Fairness with Encrypted Sensitive Attributes ICML 2018 Delayed Impact of Fair Machine Learning ICML 2018 Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer NeurIPS 2018 Residual Unfairness in Fair Machine Learning from Prejudiced Data ICML 2018 Counterfactual Fairness NeurIPS 2017