equalized odd

Fair for All: Best-effort Fairness Guarantees for Classification AISTATS 2021 Justicia: A Stochastic SAT Approach to Formally Verify Fairness AAAI 2021 Towards Reducing Biases in Combining Multiple Experts Online IJCAI 2021 Achieving Equalized Odds by Resampling Sensitive Attributes NeurIPS 2020 A Fair Classifier Using Kernel Density Estimation NeurIPS 2020 Bounding the fairness and accuracy of classifiers from population statistics ICML 2020 Conditional Learning of Fair Representations ICLR 2020 Equalized odds postprocessing under imperfect group information AISTATS 2020 Exploring Text Specific and Blackbox Fairness Algorithms in Multimodal Clinical NLP EMNLP 2020 Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics ICML 2020 Towards Threshold Invariant Fair Classification UAI 2020 Counterfactual Reasoning for Fair Clinical Risk Prediction MLHC 2019 Differentially Private Fair Learning ICML 2019 Paradoxes in Fair Machine Learning NeurIPS 2019 Equality of Opportunity in Classification: A Causal Approach NeurIPS 2018 On preserving non-discrimination when combining expert advice NeurIPS 2018 Learning Non-Discriminatory Predictors COLT 2017 Recycling Privileged Learning and Distribution Matching for Fairness NeurIPS 2017