Application Areas

Fair Classification with Adversarial Perturbations NeurIPS 2021 Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees ICML 2021 Fair Clustering Under a Bounded Cost NeurIPS 2021 Fair Exploration via Axiomatic Bargaining NeurIPS 2021 FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation WACV 2021 Fair Feature Distillation for Visual Recognition CVPR 2021 FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders ICLR 2021 Fair for All: Best-effort Fairness Guarantees for Classification AISTATS 2021 Fair Influence Maximization: a Welfare Optimization Approach AAAI 2021 Fair Mixup: Fairness via Interpolation ICLR 2021 Fairness and Bias in Online Selection ICML 2021 Fairness-aware Class Imbalanced Learning EMNLP 2021 Fairness-aware News Recommendation with Decomposed Adversarial Learning AAAI 2021 Fairness constraint of Fuzzy C-means Clustering improves clustering fairness ACML 2021 Fairness for Image Generation with Uncertain Sensitive Attributes ICML 2021 Fairness in Forecasting and Learning Linear Dynamical Systems AAAI 2021 Fairness in Influence Maximization through Randomization AAAI 2021 Fairness in Long-Term Participatory Budgeting IJCAI 2021 Fairness in Ranking under Uncertainty NeurIPS 2021 Fairness of Exposure in Stochastic Bandits ICML 2021 Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints AAAI 2021 Fairness via Representation Neutralization NeurIPS 2021 Fair Representations by Compression AAAI 2021 Fair Selective Classification Via Sufficiency ICML 2021 Fair Sequential Selection Using Supervised Learning Models NeurIPS 2021