Paper Citation Record · LEDGER

How to Protect Models against Adversarial Unlearning?

As of 9 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2507.10886.

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

pith.paper-citation-record.v1
2507.10886 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:26:34.714945Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

44 of 44 outbound references displayed

verified exact3
verified fuzzy11
unresolved27
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 756d2d76-bb08-4223-9953-94a61164a52d · outbound

This paper cites Agarwal, B.

How to Protect Models against Adversarial Unlearning? Agarwal, B

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:26:39.628835Z

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Observation a8bf19fc-b553-4c30-84f8-cb0be608e547 · outbound

This paper cites Bourtoule, V.

How to Protect Models against Adversarial Unlearning? Bourtoule, V

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:26:39.465297Z

Source-reported events for the cited work

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Observation 9046a614-d4b2-4dca-900a-9ef34e93f518 · outbound

This paper cites California consumer privacy act of 2018, 2018.

How to Protect Models against Adversarial Unlearning? California consumer privacy act of 2018, 2018

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:26:39.314998Z

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Observation 5b01c2f2-7a30-4e98-88e5-cdc990ac16f6 · outbound

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How to Protect Models against Adversarial Unlearning? Unresolved cited work

Reference 4

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Source-reported events for the cited work

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Observation ba573fc0-b7e9-470d-bf2d-6fd4f059aead · outbound

This paper cites an unresolved cited work.

How to Protect Models against Adversarial Unlearning? Unresolved cited work

Reference 5

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This paper cites an unresolved cited work.

How to Protect Models against Adversarial Unlearning? Unresolved cited work

Reference 6

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Source-reported events for the cited work

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Observation 649e5510-0196-4708-9fd9-3db8ffa2670b · outbound

This paper cites an unresolved cited work.

How to Protect Models against Adversarial Unlearning? Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:26:38.665089Z

Source-reported events for the cited work

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How to Protect Models against Adversarial Unlearning? Unresolved cited work

Reference 8

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Observation f9c3d4cf-40cb-427d-8eb5-0a4e2f2b7939 · outbound

This paper cites an unresolved cited work.

How to Protect Models against Adversarial Unlearning? Unresolved cited work

Reference 9

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This paper cites Council regulation (EU) no 269/2014, 2014.

How to Protect Models against Adversarial Unlearning? Council regulation (EU) no 269/2014, 2014

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:26:38.500393Z

Source-reported events for the cited work

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Observation f043450c-4102-4dd5-9ea0-bd3930e6cc83 · outbound

This paper cites Dhasade, Y.

How to Protect Models against Adversarial Unlearning? Dhasade, Y

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:26:38.327590Z

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Observation 13a6321d-50a0-46ce-b604-8c4d8e60ed71 · outbound

This paper cites an unresolved cited work.

How to Protect Models against Adversarial Unlearning? Unresolved cited work

Reference 12

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Source-reported events for the cited work

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Observation 6e89cea7-69ce-4676-a0fd-c0fcf50943f3 · outbound

This paper cites an unresolved cited work.

How to Protect Models against Adversarial Unlearning? Unresolved cited work

Reference 13

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Observation 8a3ad234-82b1-4666-8ff7-83dc3769109b · outbound

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How to Protect Models against Adversarial Unlearning? Unresolved cited work

Reference 14

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Observation 758860f6-8a64-42a6-a0f8-cbbb8d692b6f · outbound

This paper cites Golatkar, A.

How to Protect Models against Adversarial Unlearning? Golatkar, A

Reference 15

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This paper cites Golatkar, A.

How to Protect Models against Adversarial Unlearning? Golatkar, A

Reference 16

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Source-reported events for the cited work

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Observation 6ad2aaa9-7c24-4e51-9ce0-e7eb3e4a954a · outbound

This paper cites Golatkar, A.

How to Protect Models against Adversarial Unlearning? Golatkar, A

Reference 17

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Observation dd6ccdc6-3b6f-4e39-aadd-ccb28a3c0c1d · outbound

This paper cites Amnesiac Machine Learning.

How to Protect Models against Adversarial Unlearning? Amnesiac Machine Learning

Reference 18

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How to Protect Models against Adversarial Unlearning? Unresolved cited work

Reference 19

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How to Protect Models against Adversarial Unlearning? Unresolved cited work

Reference 20

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Source-reported events for the cited work

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Observation 2849827f-2259-4c05-96d2-629045f4ef92 · outbound

This paper cites Certified Data Removal from Machine Learning Models.

How to Protect Models against Adversarial Unlearning? Certified Data Removal from Machine Learning Models

Reference 21

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Observation 86d6337a-8f3f-4730-bb8e-8199f45b21c8 · outbound

This paper cites Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy.

How to Protect Models against Adversarial Unlearning? Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy

Reference 22

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Observation ecbe00d0-7f7b-473f-8e52-577b3fa26e76 · outbound

This paper cites SoK: Privacy-Preserving Data Synthesis.

How to Protect Models against Adversarial Unlearning? SoK: Privacy-Preserving Data Synthesis

Reference 23

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Observation 42ddc9e5-d103-47c2-99ed-1fa08c2726d1 · outbound

This paper cites Understanding Black-box Predictions via Influence Functions.

How to Protect Models against Adversarial Unlearning? Understanding Black-box Predictions via Influence Functions

Reference 24

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Observation 0ce2e430-f91c-40da-a77b-e31e0572d8f7 · outbound

This paper cites Jeong, S.

How to Protect Models against Adversarial Unlearning? Jeong, S

Reference 25

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Observation 61bec075-218b-4aef-bbfe-7c007d4b67b6 · outbound

This paper cites Lapuschkin, S.

How to Protect Models against Adversarial Unlearning? Lapuschkin, S

Reference 26

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Observation e989c6bf-2cf7-42cf-b9d6-4b04d8e6bf9b · outbound

This paper cites Krizhevsky and G.

How to Protect Models against Adversarial Unlearning? Krizhevsky and G

Reference 27

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This paper cites Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning.

How to Protect Models against Adversarial Unlearning? Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning

Reference 28

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Observation 6c6dbaed-7614-456f-bbe1-70157da217bf · outbound

This paper cites Lecun, L.

How to Protect Models against Adversarial Unlearning? Lecun, L

Reference 29

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Observation 03c3957c-8a53-4d58-acfc-dba1b84dde35 · outbound

This paper cites Certifiable Machine Unlearning for Linear Models.

How to Protect Models against Adversarial Unlearning? Certifiable Machine Unlearning for Linear Models

Reference 31

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Observation 16ae5a5c-c1af-426d-bbf4-aab0774634c5 · outbound

This paper cites New insights and perspectives on the natural gradient method.

How to Protect Models against Adversarial Unlearning? New insights and perspectives on the natural gradient method

Reference 32

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This paper cites Hard to Forget: Poisoning Attacks on Certified Machine Unlearning.

How to Protect Models against Adversarial Unlearning? Hard to Forget: Poisoning Attacks on Certified Machine Unlearning

Reference 33

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How to Protect Models against Adversarial Unlearning? Unresolved cited work

Reference 34

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How to Protect Models against Adversarial Unlearning? Unresolved cited work

Reference 35

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How to Protect Models against Adversarial Unlearning? Martens and R

Reference 36

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This paper cites An Introduction to Machine Unlearning.

How to Protect Models against Adversarial Unlearning? An Introduction to Machine Unlearning

Reference 37

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This paper cites Shaik, X.

How to Protect Models against Adversarial Unlearning? Shaik, X

Reference 38

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Observation 4dc3cc84-9564-4c84-a744-08ac86e496f6 · outbound

This paper cites A Survey of Machine Unlearning.

How to Protect Models against Adversarial Unlearning? A Survey of Machine Unlearning

Reference 39

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How to Protect Models against Adversarial Unlearning? Unresolved cited work

Reference 40

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This paper cites Machine Unlearning: Solutions and Challenges.

How to Protect Models against Adversarial Unlearning? Machine Unlearning: Solutions and Challenges

Reference 41

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How to Protect Models against Adversarial Unlearning? Thudi, H

Reference 43

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Observation c2d934f0-eed3-4463-802d-6d20f0d11fc1 · outbound

This paper cites Wallis and I.

How to Protect Models against Adversarial Unlearning? Wallis and I

Reference 44

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How to Protect Models against Adversarial Unlearning? Unresolved cited work

Reference 2021

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Observation d3b20c0b-4364-4997-8470-c7fd5c11ace0 · outbound

This paper cites Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy.

How to Protect Models against Adversarial Unlearning? Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy

Reference 2024

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Pith citing papers

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