Search Results for author:
Found 8 papers, 4 papers with code
Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment
xOrder consistently achieves a better balance between the algorithm utility and ranking fairness on a variety of datasets with different metrics.
Collaborate to Defend Against Adversarial Attacks
However, an ensemble still wastes the limited capacity of multiple models.
Correcting the User Feedback-Loop Bias for Recommendation Systems
We empirically validated the existence of such user feedback-loop bias in real world recommendation systems and compared the performance of our method with the baseline models that are either without de-biasing or with propensity scores estimated by other methods.
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
In this paper, we propose an FL framework to jointly consider performance consistency and algorithmic fairness across different local clients (data sources).
Collaboration Equilibrium in Federated Learning
Federated learning (FL) refers to the paradigm of learning models over a collaborative research network involving multiple clients without sacrificing privacy.
Unsupervised Disentanglement Learning by intervention
In this paper, we focus on the unsupervised learning of disentanglement in a general setting which the generative factors may be correlated.
Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility
Bipartite ranking, which aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data, is widely adopted in various applications where sample prioritization is needed.
