Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy | Paper Citation Record

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This paper cites https : / / www.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy https : / / www

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This paper cites Nonparametric estimation and inference about the overlap of two distributions.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Nonparametric estimation and inference about the overlap of two distributions

Reference 2

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This paper cites Membership inference at- tacks and defenses in federated learning: A survey.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Membership inference at- tacks and defenses in federated learning: A survey

Reference 3

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This paper cites Rmr: A relative membership risk measure for machine learning mod- els.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Rmr: A relative membership risk measure for machine learning mod- els

Reference 4

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Observation a6ca1b5c-7104-4d96-87a8-6750f27101a4 · outbound

This paper cites Recon- struction attacks on machine unlearning: Simple mod- els are vulnerable.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Recon- struction attacks on machine unlearning: Simple mod- els are vulnerable

Reference 5

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This paper cites Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot

Reference 6

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This paper cites Member- ship inference attacks from first principles.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Member- ship inference attacks from first principles

Reference 7

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Observation 13c0efb4-8e56-458e-a358-46e852f388c5 · outbound

This paper cites Boundary unlearning: Rapid forget- ting of deep networks via shifting the decision bound- ary.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Boundary unlearning: Rapid forget- ting of deep networks via shifting the decision bound- ary

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:47:15.098991Z

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Observation 071c0adb-6e63-49ba-8552-fba30f14d9c4 · outbound

This paper cites When machine unlearning jeopardizes privacy.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy When machine unlearning jeopardizes privacy

Reference 9

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This paper cites Chundawat, Ayush K.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Chundawat, Ayush K

Reference 10

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Observation 4cdc413d-58d6-48ba-b243-6da88d162400 · outbound

This paper cites Arcface: Additive angular margin loss for deep face recognition.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Arcface: Additive angular margin loss for deep face recognition

Reference 11

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Observation 8e6dd114-2222-4b0f-81fa-a5d759209180 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy An image is worth 16x16 words: Transformers for image recognition at scale

Reference 12

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Observation fb99f98a-0a81-4656-9990-8b8563a2d7bd · outbound

This paper cites Salun: Empow- ering machine unlearning via gradient-based weight saliency in both image classification and generation.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Salun: Empow- ering machine unlearning via gradient-based weight saliency in both image classification and generation

Reference 13

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Observation 6ee764f8-618a-4b98-a698-68a2050b6a50 · outbound

This paper cites Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts

Reference 14

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This paper cites Ethos: Rectifying language models in orthogonal parameter space.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Ethos: Rectifying language models in orthogonal parameter space

Reference 15

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Observation 1db0c169-3aaf-426c-9e8f-51b925fd3ff9 · outbound

This paper cites Eternal sunshine of the spotless net: Selec- tive forgetting in deep networks.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Eternal sunshine of the spotless net: Selec- tive forgetting in deep networks

Reference 16

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Observation c934e7aa-8078-4a3d-af03-4250f6de5c95 · outbound

This paper cites Amnesiac machine learning.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Amnesiac machine learning

Reference 17

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Observation 1c36d6cc-d748-492f-a4a8-82710948a596 · outbound

This paper cites The elements of statistical learning: data mining, inference, and prediction, vol- ume 2.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy The elements of statistical learning: data mining, inference, and prediction, vol- ume 2

Reference 18

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Observation 822c355e-7a5b-4882-884a-2d6e8182029a · outbound

This paper cites Deep residual learning for image recognition.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Deep residual learning for image recognition

Reference 19

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Observation 8b754d4b-5d49-455f-87af-775b1bc25222 · outbound

This paper cites Learn to unlearn for deep neural networks: Minimizing unlearning interference with gradient pro- jection.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Learn to unlearn for deep neural networks: Minimizing unlearning interference with gradient pro- jection

Reference 20

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Observation 0bf8a385-4810-41e0-9849-4f81cd42b7c2 · outbound

This paper cites Learn what you want to unlearn: Unlearning in- version attacks against machine unlearning.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Learn what you want to unlearn: Unlearning in- version attacks against machine unlearning

Reference 21

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Observation bf02db72-d3c4-4723-b18d-1b286a5390c2 · outbound

This paper cites Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning

Reference 22

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This paper cites Unified gradient-based machine unlearning with re- main geometry enhancement.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Unified gradient-based machine unlearning with re- main geometry enhancement

Reference 23

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Observation 327b0244-1835-45a2-b83f-583f168f68da · outbound

This paper cites Model sparsity can simplify machine unlearning.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Model sparsity can simplify machine unlearning

Reference 24

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This paper cites F? d: On under- standing the role of deep feature spaces on face gen- eration evaluation.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy F? d: On under- standing the role of deep feature spaces on face gen- eration evaluation

Reference 25

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Observation cbd71aa4-30e7-4b6c-b2ff-e1b533c92923 · outbound

This paper cites Progressive growing of gans for improved quality, stability, and variation.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Progressive growing of gans for improved quality, stability, and variation

Reference 26

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Observation 48c24ca4-362c-4a41-ab80-158b07a7f577 · outbound

This paper cites Towards unbounded machine unlearning.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Towards unbounded machine unlearning

Reference 27

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Observation b9d00b76-45dc-4e9a-be8b-2bbac974d583 · outbound

This paper cites A sample-level evaluation and generative framework for model inver- sion attacks.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy A sample-level evaluation and generative framework for model inver- sion attacks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:47:14.911919Z

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Observation c20a3b60-fff2-4673-8b1a-c9f9ea7f8bd6 · outbound

This paper cites FUNU: boosting machine unlearning efficiency by filtering unnecessary unlearning.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy FUNU: boosting machine unlearning efficiency by filtering unnecessary unlearning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:47:14.900434Z

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Observation 11a80319-9feb-48d1-87df-da3c3841a30c · outbound

This paper cites A data- free backdoor injection approach in neural networks.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy A data- free backdoor injection approach in neural networks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:47:14.889661Z

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Observation ee1c4257-edef-44fe-b97f-6f234ce766ae · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 31

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Observation 580efdb3-34fe-4193-9d65-227c8f1f6ce3 · outbound

This paper cites The eu proposal for a general data protection regulation and the roots of the ‘right to be forgotten’.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy The eu proposal for a general data protection regulation and the roots of the ‘right to be forgotten’

Reference 32

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Observation 31f59c8b-fda1-44c1-ac05-d5b359603dad · outbound

This paper cites No-reference image quality assessment in the spatial domain.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy No-reference image quality assessment in the spatial domain

Reference 33

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Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Unresolved cited work

Reference 34

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Observation ce2f2f6a-2a10-47ee-aa60-5b5013e53018 · outbound

This paper cites High- resolution image synthesis with latent diffusion mod- els.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy High- resolution image synthesis with latent diffusion mod- els

Reference 35

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Observation bc7bd329-da6d-42fb-a36e-77e79f1ba31b · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 36

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Observation d909291f-748e-4677-9d13-72fec883e5d9 · outbound

This paper cites Cluster quality analysis using silhouette score.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Cluster quality analysis using silhouette score

Reference 37

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Observation 7f0114e5-7091-4a4a-93eb-4dd7bf640838 · outbound

This paper cites Membership inference attacks against machine learning models.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Membership inference attacks against machine learning models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:47:14.818254Z

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Observation b4b314c9-bd3f-4a71-9cb2-f436f0007830 · outbound

This paper cites Unrolling sgd: Understand- ing factors influencing machine unlearning.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Unrolling sgd: Understand- ing factors influencing machine unlearning

Reference 39

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This paper cites Data-free model extraction.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Data-free model extraction

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:47:14.797114Z

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Observation 0627be5a-5c21-4e97-b17a-94f03a69e080 · outbound

This paper cites Visu- alizing data using t-sne.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Visu- alizing data using t-sne

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:47:14.787785Z

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Observation 99b8278b-ec69-4832-91d3-72dc19b30f65 · outbound

This paper cites Anti- dreambooth: Protecting users from personalized text- to-image synthesis.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Anti- dreambooth: Protecting users from personalized text- to-image synthesis

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:47:14.777915Z

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Observation fc36a255-86af-4210-936e-1012cd20cf4e · outbound

This paper cites Precise, fast, and low-cost concept erasure in value space: Or- thogonal complement matters.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Precise, fast, and low-cost concept erasure in value space: Or- thogonal complement matters

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:47:14.766425Z

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Observation 79230b04-cc7a-4c0c-92c7-e07ee3ec77d2 · outbound

This paper cites Machine unlearning of features and labels.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Machine unlearning of features and labels

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:47:14.755496Z

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Observation 05d01d86-dd30-46c2-9874-d05074f18553 · outbound

This paper cites Mexmi: Pool-based active model extraction crossover membership infer- ence.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy Mexmi: Pool-based active model extraction crossover membership infer- ence

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:47:14.744681Z

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Observation 854485a1-0142-49f8-8509-30141c7961c6 · outbound

This paper cites To generate or not? safety-driven unlearned diffusion models are still easy to generate unsafe im- ages.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy To generate or not? safety-driven unlearned diffusion models are still easy to generate unsafe im- ages

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:47:14.733724Z

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Observation 43fcb0bb-5f00-45a3-be81-9093509d4c15 · outbound

This paper cites unlearning-by-disobedience.

Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy unlearning-by-disobedience

Reference 47

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