3 papers
cs.LG2026
Less Noise, Same Certificate: Retain Sensitivity for Unlearning
Carolin Heinzler, Kasra Malihi, Amartya Sanyal
Certified machine unlearning aims to provably remove the influence of a deletion set from a model trained on a dataset , by producing an unlearned output that is statistical…
cs.LG2025
Learning in an Echo Chamber: Online Learning with Replay Adversary
Daniil Dmitriev, Harald Eskelund Franck, Carolin Heinzler +1
As machine learning systems increasingly train on self-annotated data, they risk reinforcing errors and becoming echo chambers of their own beliefs. We model this phenomenon by int…
cs.LG2025
Adversarial Resilience against Clean-Label Attacks in Realizable and Noisy Settings
Carolin Heinzler
We investigate the challenge of establishing stochastic-like guarantees when sequentially learning from a stream of i.i.d. data that includes an unknown quantity of clean-label adv…