I am a PhD fellow at the Machine Learning Section of the University of Copenhagen
with a focus on Unlearning, Robustness and Privacy.
I am supervised by Prof. Amartya Sanyal
and Prof. Amir Yehudayoff and part of the Foundations of
Responsible Machine Learning Group (Cope-FoRML).
| SODA 2026 |
Learning in an Echo Chamber: Online Learning with Replay Adversary Daniil Dmitriev, Harald Eskelund Franck, Carolin Heinzler, Amartya Sanyal$^*$ | arXiv, 2025 | $^*\alpha\beta$-cal order When models train on their own past guesses, mistakes can echo and mislead learning. We introduce a learning-theoretic setting that models this phenomenon: Online Learning in the Replay Setting. We introduce a combinatorial measure, the Extended Threshold dimension, which characterises learnability in this setting. |
| Master's thesis |
Adversarial Resilience against Clean-Label Attacks in Realizable and Noisy Settings Carolin Heinzler | arXiv, 2024 We investigate the challenge of establishing stochastic-like guarantees when learning from a stream of i.i.d. data with clean-label adversarial samples. Introducing the notion of a clean-label adversary in the agnostic context, we are the first to give a theoretical analysis of a disagreement-based learner for thresholds. |
| Reviewer | NeurIPS 2025 Workshop Reliable ML, AISTATS 2025 |
| P1 Program | Member of P1 Program: Data Privacy in Machine Learning of the Pioneer Center for AI, Denmark |
| Local Organizer |
Affinity Event of the Learning Theory Alliance at EurIPS 2025 in Copenhagen, Denmark IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) 2025 in Copenhagen, Denmark |
| Representation | PhD student representative in LSU committee for Computer Science Department, University of Copenhagen (2024-ongoing) |
| Teaching Assistant |
Machine Learning A (2025, University of Copenhagen), Mathematics of Signals, Networks and Learning (2024, ETH Zurich), Quantitative Risk Management (2024, ETH Zurich), Probability Theory and Statistics (2023, ETH Zurich), Introduction to Mathematics (2021-2022, WU Vienna), Introduction to Phyton (2019-2021, University of Vienna) |