2 papers
cs.LG2026
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.LG2025
Explainable Clustering Beyond Worst-Case Guarantees
Maximilian Fleissner, Maedeh Zarvandi, Debarghya Ghoshdastidar
We study the explainable clustering problem first posed by Moshkovitz, Dasgupta, Rashtchian, and Frost (ICML 2020). The goal of explainable clustering is to fit an axis-aligned dec…