 Adversarial Learning for Counterfactual FairnessMachine-mediated learning (ML), 2020 |
 Ensuring Fairness under Prior Probability ShiftsAAAI/ACM Conference on AI, Ethics, and Society (AIES), 2020 |
 FACT: A Diagnostic for Group Fairness Trade-offsInternational Conference on Machine Learning (ICML), 2020 |
 FairALM: Augmented Lagrangian Method for Training Fair Models with
Little RegretEuropean Conference on Computer Vision (ECCV), 2020 |
 Learning Fairness-aware Relational StructuresEuropean Conference on Artificial Intelligence (ECAI), 2020 Yue Zhang Arti Ramesh |
 To Split or Not to Split: The Impact of Disparate Treatment in
ClassificationIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2020 |
 Fair Active LearningExpert systems with applications (ESWA), 2020 |
 FANNet: Formal Analysis of Noise Tolerance, Training Bias and Input
Sensitivity in Neural NetworksDesign, Automation and Test in Europe (DATE), 2019 |
 Fair Adversarial Gradient Tree BoostingIndustrial Conference on Data Mining (IDM), 2019 |
 Assessing Social and Intersectional Biases in Contextualized Word
RepresentationsNeural Information Processing Systems (NeurIPS), 2019 |
 Predictive Multiplicity in ClassificationInternational Conference on Machine Learning (ICML), 2019 |
 Towards Reducing Biases in Combining Multiple Experts OnlineInternational Joint Conference on Artificial Intelligence (IJCAI), 2019 |
 Rényi Fair InferenceInternational Conference on Learning Representations (ICLR), 2019 |
 Coresets for Clustering with Fairness ConstraintsNeural Information Processing Systems (NeurIPS), 2019 |
 Optimized Score Transformation for Consistent Fair ClassificationJournal of machine learning research (JMLR), 2019 |
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