We consider the problem of spectral clustering under group fairness constraints, where
samples from each sensitive group are approximately proportionally represented in each …

Classical clustering methods do not provide users with direct control of the clustering results,
and the clustering results may not be consistent with the relevant criterion that a user has in …

Facilitated by the powerful feature extraction ability of neural networks, deep clustering has
achieved great success in analyzing high-dimensional and complex real-world data. The …

We propose a novel approach to mitigate biases in computer vision models by utilizing
counterfactual generation and fine-tuning. While counterfactuals have been used to analyze …

Robust Unsupervised Domain Adaptation (RoUDA) aims to achieve not only clean but also
robust cross-domain knowledge transfer from a labeled source domain to an unlabeled …

Fair clustering aims to partition a dataset while mitigating bias in the original dataset.
Developing fair clustering algorithms has gained increasing attention from the machine …

We consider the problem of deep fair clustering, which partitions data into clusters via the
representations extracted by deep neural networks while hiding sensitive data attributes. To …

We study the canonical fair clustering problem where each cluster is constrained to have
close to population-level representation of each group. Despite significant attention, the …

Representation Learning for Clustering Page 1 Tutorial: Application of Deep Clustering Algorithms
32nd ACM International Conference on Information and Knowledge Management 1Institute of …