Showing 1–18 of 18 results
/ Date/ Name
Dec 20, 2025Conscious Data Contribution via Community-Driven Chain-of-Thought DistillationMay 14, 2025Towards Fair In-Context Learning with Tabular Foundation ModelsMay 9, 2025Crowding Out The Noise: Algorithmic Collective Action Under Differential PrivacyNov 22, 2024Adaptive Group Robust Ensemble Knowledge DistillationMar 18, 2024Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule ListsDec 22, 2023SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine LearningAug 29, 2023Probabilistic Dataset Reconstruction from Interpretable ModelsJul 24, 2023Fairness Under Demographic Scarce RegimeMar 8, 2023Learning Hybrid Interpretable Models: Theory, Taxonomy, and MethodsSep 2, 2022Exploiting Fairness to Enhance Sensitive Attributes ReconstructionMay 30, 2022Fool SHAP with Stealthily Biased SamplingMay 15, 2022Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc ExplanationsJun 14, 2021Characterizing the risk of fairwashingSep 3, 2020Model extraction from counterfactual explanationsSep 26, 2019GAMIN: An Adversarial Approach to Black-Box Model InversionSep 9, 2019Learning Fair Rule ListsJun 19, 2019Adversarial training approach for local data debiasingJan 28, 2019Fairwashing: the risk of rationalization