Paper Citation Record · LEDGER

Efficient Machine Unlearning by Model Splitting and Core Sample Selection

As of 23 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2505.07026.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.07026 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:36:06.707062Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

verified exact0
verified fuzzy26
unresolved11
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fe6058ef-67e0-4b3b-96c0-1f97b7160573 · outbound

This paper cites General data protection regulation,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection General data protection regulation,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.517854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 293feb2b-6ae8-491e-a649-45a020c2fa84 · outbound

This paper cites California consumer privacy act of 2018,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection California consumer privacy act of 2018,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.505663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e8a33f1f-342f-4dd4-9727-b6474226e9ec · outbound

This paper cites Machine unlearning,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Machine unlearning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.493335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8f442f34-b9fa-4c01-a254-d6a6e81f8327 · outbound

This paper cites Graph unlearning,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Graph unlearning,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.481311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a3000bfb-c7b9-4b71-9810-32ae49fd8835 · outbound

This paper cites Making AI forget you: Data deletion in machine learning,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Making AI forget you: Data deletion in machine learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.469498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ce66ec46-aa92-446b-a6f4-e60f6e6a3d26 · outbound

This paper cites Towards making systems forget with machine unlearning,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Towards making systems forget with machine unlearning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.457686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ca651079-ba52-4194-8ffe-ae736452bb70 · outbound

This paper cites Certified data removal from machine learning models,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Certified data removal from machine learning models,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.445677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d4be1d5c-acc2-462f-89e5-8e1ac19c58da · outbound

This paper cites Machine Unlearning of Features and Labels.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Machine Unlearning of Features and Labels

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T22:36:06.567090Z

Source-reported events for the cited work

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Observation c19e9ba9-19e3-440a-a874-6279e2108169 · outbound

This paper cites Algorithms that approximate data removal: New results and limitations,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Algorithms that approximate data removal: New results and limitations,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.433211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 39dae111-96ab-4649-92aa-4a52eeae1224 · outbound

This paper cites Deep unlearning via ran- domized conditionally independent hessians,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Deep unlearning via ran- domized conditionally independent hessians,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.419013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bad1160e-eeeb-401b-a52d-9abf1f041298 · outbound

This paper cites Eternal sunshine of the spotless net: Selective forgetting in deep networks,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Eternal sunshine of the spotless net: Selective forgetting in deep networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.405777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4e8ee7bb-be84-490e-b7ed-0be7e630bf18 · outbound

This paper cites Deltagrad: Rapid retraining of machine learning models,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Deltagrad: Rapid retraining of machine learning models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.392394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cd69ba49-979d-40e6-a000-8cffbc3ce38e · outbound

This paper cites Fedrecover: Recovering from poisoning attacks in federated learning using historical information,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Fedrecover: Recovering from poisoning attacks in federated learning using historical information,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.378778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 569a61bb-f196-4e5a-9017-b074babe40d4 · outbound

This paper cites A Survey of Machine Unlearning.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection A Survey of Machine Unlearning

Reference 14

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Source-reported events for the cited work

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Observation 94265aaf-44ec-4941-a38c-0127e7b9940b · outbound

This paper cites Machine unlearning: A survey,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Machine unlearning: A survey,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.366737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ae0176be-8653-4aa9-8be6-7f818e200dae · outbound

This paper cites Machine Unlearning: A Comprehensive Survey.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Machine Unlearning: A Comprehensive Survey

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T22:36:06.608934Z

Source-reported events for the cited work

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Observation c00d22ac-a29d-4284-a020-c1a63166fb63 · outbound

This paper cites A survey on machine unlearning: Techniques and new emerged privacy risks,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection A survey on machine unlearning: Techniques and new emerged privacy risks,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T22:36:06.614383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a45b8fd0-a11e-453b-8bb9-414dd6e38465 · outbound

This paper cites Threats, attacks, and defenses in machine unlearning: A survey,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Threats, attacks, and defenses in machine unlearning: A survey,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.345562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 86e8a818-bfb0-4f41-a641-4c46021a1ac1 · outbound

This paper cites Machine un- learning of federated clusters,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Machine un- learning of federated clusters,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.333319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation eb3e6abc-9e97-4759-81f4-860a87af05dd · outbound

This paper cites An information theoretic approach to machine unlearning,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection An information theoretic approach to machine unlearning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.321104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0a0521c6-c3f0-44f4-9346-7b4d714c1190 · outbound

This paper cites Model sparsity can simplify machine unlearning,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Model sparsity can simplify machine unlearning,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T22:36:06.632585Z

Source-reported events for the cited work

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Observation 1b1fef2d-5e49-4714-8025-c9f5351ca02a · outbound

This paper cites Fast yet effective machine unlearning,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Fast yet effective machine unlearning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.299916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0a26ec0d-ad36-4962-89a3-e93c3d270f26 · outbound

This paper cites Certified Data Removal from Machine Learning Models.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Certified Data Removal from Machine Learning Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T22:36:06.641159Z

Source-reported events for the cited work

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Observation 2f4957c7-1618-4ee5-b4e4-47e5e5bdc3af · outbound

This paper cites Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.285891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f2b82a6f-5db6-4f9a-9d32-a88b858dc20a · outbound

This paper cites En- hanced membership inference attacks against machine learning models,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection En- hanced membership inference attacks against machine learning models,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.273563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b20baa2b-a214-492c-be68-aeb7e057f1c4 · outbound

This paper cites A survey on membership inference attacks and defenses in machine learning,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection A survey on membership inference attacks and defenses in machine learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.260507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 894f9757-f4d9-432d-a05e-d1c49ef85b9c · outbound

This paper cites Towards Probabilistic Verification of Machine Unlearning.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Towards Probabilistic Verification of Machine Unlearning

Reference 27

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Source-reported events for the cited work

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Observation fcf7c987-c9b4-4a05-bb0d-459fbda7934b · outbound

This paper cites An informa- tion theoretic evaluation metric for strong unlearning,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection An informa- tion theoretic evaluation metric for strong unlearning,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T22:36:06.667496Z

Source-reported events for the cited work

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Observation 7b354605-2f96-42ae-9709-07d1cff30e09 · outbound

This paper cites Verification of machine unlearning is fragile,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Verification of machine unlearning is fragile,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.246628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5f7037d6-eea1-4f06-b42c-6a84212bd4da · outbound

This paper cites Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T22:36:06.676088Z

Source-reported events for the cited work

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Observation a687f264-09a3-4a0d-af68-332bcea30cf2 · outbound

This paper cites Privacy Auditing of Large Language Models.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Privacy Auditing of Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T22:36:06.680767Z

Source-reported events for the cited work

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Observation e93a94b7-19c3-4554-931d-dc53a64edc10 · outbound

This paper cites Membership inference attacks and defenses in classification models,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Membership inference attacks and defenses in classification models,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.232470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3ae8f258-0137-4618-9182-6a0fbc7a9cfb · outbound

This paper cites Towards deep learning models resistant to adversarial attacks,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Towards deep learning models resistant to adversarial attacks,

Reference 33

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Source-reported events for the cited work

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Observation e72cd1f7-4260-4d18-af3c-97a789ebb382 · outbound

This paper cites Regularization mixup adversarial training: A defense strategy for membership privacy with model availability assurance,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Regularization mixup adversarial training: A defense strategy for membership privacy with model availability assurance,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.209483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 917a6421-0257-4fae-a1af-3ea3e692379e · outbound

This paper cites Membership inference attacks against adversarially robust deep learning models,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Membership inference attacks against adversarially robust deep learning models,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.067678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 79e5c38d-999f-4a3d-b56d-f20cd3bbb987 · outbound

This paper cites Unrolling sgd: Understanding factors influencing machine unlearning,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Unrolling sgd: Understanding factors influencing machine unlearning,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.031315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3198eb3d-4599-4a0e-a223-7d6aa85dcee8 · outbound

This paper cites Gradient-based learning applied to document recognition,.

Efficient Machine Unlearning by Model Splitting and Core Sample Selection Gradient-based learning applied to document recognition,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:36:07.017566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.