206 citations · 315 across the 22 of their papers we have counts for
23 papers
Scalable Spectral Clustering with Group Fairness Constraints
Ji Wang, Ding Lu, Ian Davidson +1
There are synergies of research interests and industrial efforts in modeling fairness and correcting algorithmic bias in machine learning. In this paper, we present a scalable algo…
Towards Auditing Unsupervised Learning Algorithms and Human Processes For Fairness
Ian Davidson, S. S. Ravi
Existing work on fairness typically focuses on making known machine learning algorithms fairer. Fair variants of classification, clustering, outlier detection and other styles of a…
Explainable Clustering via Exemplars: Complexity and Efficient Approximation Algorithms
Ian Davidson, Michael Livanos, Antoine Gourru +3
Explainable AI (XAI) is an important developing area but remains relatively understudied for clustering. We propose an explainable-by-design clustering approach that not only finds…
Deep Fair Discriminative Clustering
Hongjing Zhang, Ian Davidson
Deep clustering has the potential to learn a strong representation and hence better clustering performance compared to traditional clustering methods such as -means and spectral…
Deep Descriptive Clustering
Hongjing Zhang, Ian Davidson
Recent work on explainable clustering allows describing clusters when the features are interpretable. However, much modern machine learning focuses on complex data such as images,…
A Framework for Deep Constrained Clustering
Hongjing Zhang, Tianyang Zhan, Sugato Basu +1
The area of constrained clustering has been extensively explored by researchers and used by practitioners. Constrained clustering formulations exist for popular algorithms such as…