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Machine Unlearning

I did my PhD research on making machine learning models forget undesired data without having to retrain the whole model ($$$). This can be privacy infringing, copyrighted, erroneous, poisoned, market moving, outdated or otherwise problematic data.

unlearning_explain (1)



Research

🪄:  Machine Unlearning Papers
Visual Title Authorship Venue
Screenshot 2026-09-17 at 21 19 03 Redirection for Erasing Memory (REM): Towards a universal unlearning method for corrupted data First ICLR 2026
poison_wide Potion: Towards Poison Unlearning First Journal of Data-Centric Machine Learning Research (DMLR)
image Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening Equal Contrib. AAAI 2024
image Loss-Free Machine Unlearning Equal Contrib. ICLR 2024 Tiny Paper
image Parameter-Tuning-Free Data Entry Error Unlearning with Adaptive Selective Synaptic Dampening First Preprint
image Zero-Shot Machine Unlearning at Scale via Lipschitz Regularization 3rd TMLR
image Learning to Forget using Hypernetworks Co-supervised MPhil thesis NeurIPS 2024 Workshop
image CONDA: Fast Federated Unlearning with Contribution Dampening 4th Preprint
📖:  Other Research (Red Teaming, Agentic LLMs, and more)
Visual Title Authorship Venue
Screenshot 2024-08-16 at 14 56 29 Identifying contributors to manufacturing outcomes in a multi-echelon setting: a decentralised uncertainty quantification approach First IEEE Transactions on Industrial Informatics
packing Using Reinforcement Learning for the Three-Dimensional Loading Capacitated Vehicle Routing Problem First IJCAI 2023 Workshop
image Attack Atlas: A Practitioner’s Perspective on Challenges and Pitfalls in Red Teaming GenAI 2nd NeurIPS 2024 Workshop
image Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seeking Co-supervised MSc thesis IJPR

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🖋️:  A tiny thing I made to make Google Scholar better

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