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20222026
most citedIdentifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach

8 citations · 13 across the 10 of their papers we have counts for

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8 papers · 1 filter

cs.LG2024

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.…

cs.LG2024★ 1 cited

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…

cs.LG2024

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…

cs.LG2024

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…

cs.LG2024

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…

cs.LG2023

Towards Robust Continual Learning with Bayesian Adaptive Moment Regularization

Jack Foster, Alexandra Brintrup

The pursuit of long-term autonomy mandates that machine learning models must continuously adapt to their changing environments and learn to solve new tasks. Continual learning seek…

cs.LG2023★ 3 cited

Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening

Jack Foster, Stefan Schoepf, Alexandra Brintrup

Machine unlearning, the ability for a machine learning model to forget, is becoming increasingly important to comply with data privacy regulations, as well as to remove harmful, ma…

cs.LG2023★ 8 cited

Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach

Stefan Schoepf, Jack Foster, Alexandra Brintrup

Organisations often struggle to identify the causes of change in metrics such as product quality and delivery duration. This task becomes increasingly challenging when the cause li…