8 citations · 17 across the 16 of their papers we have counts for
8 papers · 1 filter
Learning to Forget using Hypernetworks
Jose Miguel Lara Rangel, Stefan Schoepf, Jack Foster +2
Machine unlearning is gaining increasing attention as a way to remove adversarial data poisoning attacks from already trained models and to comply with privacy and AI regulations.…
Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seeking
Valeria Jannelli, Stefan Schoepf, Matthias Bickel +2
This paper explores how Large Language Models (LLMs) can automate consensus-seeking in supply chain management (SCM), where frequent decisions on problems such as inventory levels…
ConDa: Fast Federated Unlearning with Contribution Dampening
Vikram S Chundawat, Pushkar Niroula, Prasanna Dhungana +3
Federated learning (FL) has enabled collaborative model training across decentralized data sources or clients. While adding new participants to a shared model does not pose great t…
Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI
Ambrish Rawat, Stefan Schoepf, Giulio Zizzo +10
As generative AI, particularly large language models (LLMs), become increasingly integrated into production applications, new attack surfaces and vulnerabilities emerge and put a f…
Potion: Towards Poison Unlearning
Stefan Schoepf, Jack Foster, Alexandra Brintrup
Adversarial attacks by malicious actors on machine learning systems, such as introducing poison triggers into training datasets, pose significant risks. The challenge in resolving…
Loss-Free Machine Unlearning
Jack Foster, Stefan Schoepf, Alexandra Brintrup
We present a machine unlearning approach that is both retraining- and label-free. Most existing machine unlearning approaches require a model to be fine-tuned to remove information…
Parameter-tuning-free data entry error unlearning with adaptive selective synaptic dampening
Stefan Schoepf, Jack Foster, Alexandra Brintrup
Data entry constitutes a fundamental component of the machine learning pipeline, yet it frequently results in the introduction of labelling errors. When a model has been trained on…
An Information Theoretic Approach to Machine Unlearning
Jack Foster, Kyle Fogarty, Stefan Schoepf +3
To comply with AI and data regulations, the need to forget private or copyrighted information from trained machine learning models is increasingly important. The key challenge in u…