Dataset for the evaluation of data unlearning techniques using KLOM (KL divergence of Margins). How KLOM works: KLOM works by: 1. training N models (original models) 2. Training N fully retrained models (oracles) on forget set F 3. unlearning forget set F from the original models 4. Comparing the outputs of the unlearned models from the retrained models on different points (specifically, computing the KL divergence between the distribution of margins of oracle models and distribution of margins of the unlearned models) Originally proposed in the work Attribute to Delete: Machine Unlearning via Datamodel Matching (https://arxiv.org/abs/2410.23232), described in detail in E.1. Outline of how KLOM works: Algorithm Description: Structure of Data The overal structure is as follows: Each folder has train logits .pt logits at the end of training for model for validation points val logits .pt logits at the end of training for model for train points val margins .npy margins of model at epoch (this is derived from logits) sd epoch .pt model checkpoint at epoch How to download Create script download folder.sh Example how to run script:
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