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Found 6 papers, 3 papers with code
Loss-Free Machine Unlearning
Most existing machine unlearning approaches require a model to be fine-tuned to remove information while preserving performance.
Parameter-tuning-free data entry error unlearning with adaptive selective synaptic dampening
Second, we demonstrate the performance of ASSD in a supply chain delay prediction problem with labelling errors using real-world data where we randomly introduce various levels of labelling errors.
Zero-Shot Machine Unlearning at Scale via Lipschitz Regularization
The key challenge in unlearning is forgetting the necessary data in a timely manner, while preserving model performance.
Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening
We present Selective Synaptic Dampening (SSD), a novel two-step, post hoc, retrain-free approach to machine unlearning which is fast, performant, and does not require long-term storage of the training data.
Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach
Organisations often struggle to identify the causes of change in metrics such as product quality and delivery duration.
Unlocking Carbon Reduction Potential with Reinforcement Learning for the Three-Dimensional Loading Capacitated Vehicle Routing Problem
Our model not only represents a promising first step towards large-scale logistics optimisation with reinforcement learning but also lays the foundation for this research stream.
