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cs.LG2025
Interpretable Self-Supervised Learning via Representer Landmarks and Nyström Approximation
Maedeh Zarvandi, Michael Timothy, Theresa Wasserer +1
Self-supervised learning (SSL) learns representations from massive unlabeled data, yet the resulting models typically operate as black boxes, necessitating domain-specific explanat…
cs.LG2024
Explainable Clustering of Mixture Models
Maximilian Fleissner, Maedeh Zarvandi, Debarghya Ghoshdastidar
The explainable clustering problem was first posed by Moshkovitz et al. (ICML 2020) and studies how well an axis-aligned decision tree with leaves can approximate a given clust…