Search Results for author:
Found 7 papers, 4 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.
Towards Robust Continual Learning with Bayesian Adaptive Moment Regularization
Continual learning seeks to overcome the challenge of catastrophic forgetting, where learning to solve new tasks causes a model to forget previously learnt information.
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.
Tropical Grassmannians, cluster algebras and scattering amplitudes
A finite cluster algebra provides a natural triangulation for the tropical Grassmannian whose volume computes the scattering amplitudes.
High Energy Physics - Theory
