Paper published in PNAS

Robust Continuous Clustering was published in the Proceedings of the National Academy of Sciences (PNAS). The paper presents a clustering algorithm that optimizes a smooth global objective using efficient numerical methods. This allows clustering to be integrated into end-to-end feature learning pipelines. Our algorithm effectively untangles mixed clusters, achieves high accuracy across domains, and scales to high dimensions and large datasets.