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  1. .gitattributes +64 -0
  2. README.md +80 -0
  3. data/living17/classes.pt +3 -0
  4. data/living17/living17_tr.beton +3 -0
  5. data/living17/living17_val.beton +3 -0
  6. data/living17/new_split/tensors.pt +3 -0
  7. data/living17/new_split/test.beton +3 -0
  8. data/living17/new_split/train.beton +3 -0
  9. data/living17/new_split/val.beton +3 -0
  10. data/living17/random_half_indices.npy +3 -0
  11. data/living17/raw_tensors_tr.pt +3 -0
  12. data/living17/raw_tensors_tr_new.pt +3 -0
  13. data/living17/raw_tensors_val.pt +3 -0
  14. data/living17/raw_tensors_val_new.pt +3 -0
  15. data/living17/train_indices.npy +3 -0
  16. data/living17/val_indices.npy +3 -0
  17. forget_set_indices/CIFAR10/forget_set_1.npy +3 -0
  18. forget_set_indices/CIFAR10/forget_set_10.npy +3 -0
  19. forget_set_indices/CIFAR10/forget_set_2.npy +3 -0
  20. forget_set_indices/CIFAR10/forget_set_3.npy +3 -0
  21. forget_set_indices/CIFAR10/forget_set_4.npy +3 -0
  22. forget_set_indices/CIFAR10/forget_set_5.npy +3 -0
  23. forget_set_indices/CIFAR10/forget_set_6.npy +3 -0
  24. forget_set_indices/CIFAR10/forget_set_7.npy +3 -0
  25. forget_set_indices/CIFAR10/forget_set_8.npy +3 -0
  26. forget_set_indices/CIFAR10/forget_set_9.npy +3 -0
  27. forget_set_indices/LIVING17/forget_set_1.npy +3 -0
  28. forget_set_indices/LIVING17/forget_set_2.npy +3 -0
  29. forget_set_indices/LIVING17/forget_set_3.npy +3 -0
  30. forget_set_indices/LIVING17/forget_set_4.npy +3 -0
  31. forget_set_indices/LIVING17/forget_set_5.npy +3 -0
  32. forget_set_indices/QNLI/forget_set_1.npy +3 -0
  33. forget_set_indices/QNLI/forget_set_2.npy +3 -0
  34. forget_set_indices/QNLI/forget_set_3.npy +3 -0
  35. forget_set_indices/QNLI/forget_set_4.npy +3 -0
  36. full_models/CIFAR10/sd_0____epoch_23.pt +3 -0
  37. full_models/CIFAR10/sd_100____epoch_23.pt +3 -0
  38. full_models/CIFAR10/sd_101____epoch_23.pt +3 -0
  39. full_models/CIFAR10/sd_102____epoch_23.pt +3 -0
  40. full_models/CIFAR10/sd_103____epoch_23.pt +3 -0
  41. full_models/CIFAR10/sd_104____epoch_23.pt +3 -0
  42. full_models/CIFAR10/sd_105____epoch_23.pt +3 -0
  43. full_models/CIFAR10/sd_106____epoch_23.pt +3 -0
  44. full_models/CIFAR10/sd_107____epoch_23.pt +3 -0
  45. full_models/CIFAR10/sd_108____epoch_23.pt +3 -0
  46. full_models/CIFAR10/sd_109____epoch_23.pt +3 -0
  47. full_models/CIFAR10/sd_10____epoch_23.pt +3 -0
  48. full_models/CIFAR10/sd_110____epoch_23.pt +3 -0
  49. full_models/CIFAR10/sd_111____epoch_23.pt +3 -0
  50. full_models/CIFAR10/sd_112____epoch_23.pt +3 -0
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ license: mit
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+ size_categories:
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+ - 10K<n<100K
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+ ---
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+ Dataset for the evaluation of data-unlearning techniques using KLOM (KL-divergence of Margins).
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+
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+ # How KLOM works:
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+ KLOM works by:
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+ 1. training N models (original models)
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+ 2. Training N fully-retrained models (oracles) on forget set F
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+ 3. unlearning forget set F from the original models
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+ 4. Comparing the outputs of the unlearned models from the retrained models on different points
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+ (specifically, computing the KL divergence between the distribution of _margins_ of oracle models and distribution of _margins_ of the unlearned models)
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+
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+ 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.
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+
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+ **Outline of how KLOM works:**
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+ ![Image 5-4-25 at 9.21 PM.jpg](https://cdn-uploads.huggingface.co/production/uploads/6625510c9277b825c8c71418/RcbE1ucGOYgTnoRJmSKa4.jpeg)
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+
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+
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+ **Algorithm Description:**
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+ ![Image 5-4-25 at 9.24 PM.jpg](https://cdn-uploads.huggingface.co/production/uploads/6625510c9277b825c8c71418/N3vJmc6rfQ5MLMjXSCIGZ.jpeg)
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+
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+ # Structure of Data
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+
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+ The overal structure is as follows:
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+ ```
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+ full_models
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+ ├── CIFAR10
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+ ├── CIFAR10_augmented
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+ └── LIVING17
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+ oracles
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+ └── CIFAR10
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+ ├── forget_set_1
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+ ├── forget_set_2
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+ ├── forget_set_3
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+ ├── forget_set_4
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+ ├── forget_set_5
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+ ├── forget_set_6
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+ ├── forget_set_7
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+ ├── forget_set_8
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+ ├── forget_set_9
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+ └── forget_set_10
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+ ```
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+ Each folder has
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+
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+ * train_logits_##.pt - logits at the end of training for model `##` for validation points
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+ * val_logits_##.pt - logits at the end of training for model `##` for train points
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+ * `##__val_margins_#.npy` - margins of model `##` at epoch `#` (this is derived from logits)
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+ * `sd_##____epoch_#.pt` - model `##` checkpoint at epoch `#`
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+
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+
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+ # How to download
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+
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+
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+
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+ Create script `download_folder.sh`
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+
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+ ```
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+ #!/bin/bash
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+ REPO_URL=https://huggingface.co/datasets/royrin/KLOM-models
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+ TARGET_DIR=KLOM-models # name it what you wish
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+ FOLDER=$1 # e.g., "oracles/CIFAR10/forget_set_3"
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+
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+ mkdir -p $TARGET_DIR
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+
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+ git clone --filter=blob:none --no-checkout $REPO_URL $TARGET_DIR
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+ cd $TARGET_DIR
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+ git sparse-checkout init --cone
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+ git sparse-checkout set $FOLDER
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+ git checkout main
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+ ```
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+
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+ Example how to run script:
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+ ```
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+ bash download_folder.sh oracles/CIFAR10/forget_set_3
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+ ```
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+
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+
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