Paper Citation Record · LEDGER

Adversarial Mixup Unlearning

As of 10 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2502.10288.

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

pith.paper-citation-record.v1
2502.10288 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T18:45:26.570940Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

14 of 14 outbound references displayed

verified exact0
verified fuzzy8
unresolved5
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e13f8ffe-564f-45f1-97c9-b59fe3c17bf4 · outbound

This paper cites an unresolved cited work.

Adversarial Mixup Unlearning Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-07T18:45:26.728371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bfbe38b6-3bb3-4222-8668-6d339c292d01 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Adversarial Mixup Unlearning Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T18:45:26.523364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e354d03-1ef8-4737-96d2-69aa67776696 · outbound

This paper cites an unresolved cited work.

Adversarial Mixup Unlearning Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-07T18:45:26.804858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 88728aec-d81c-4423-8907-eaff6945a73a · outbound

This paper cites Unroll (Thudi et al.,.

Adversarial Mixup Unlearning Unroll (Thudi et al.,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:45:26.771106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b809f0ba-1870-4c13-a633-21a2698ea202 · outbound

This paper cites For RandLabel, we tune the weight for retaining loss from {0.0001, 0.01, 0.1, 1, 10, 100}.

Adversarial Mixup Unlearning For RandLabel, we tune the weight for retaining loss from {0.0001, 0.01, 0.1, 1, 10, 100}

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:45:26.753202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ffcf59b9-08b2-4746-afe9-12e178eb6923 · outbound

This paper cites an unresolved cited work.

Adversarial Mixup Unlearning Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-07T18:45:26.787410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4ded80bd-66a7-49cf-9701-ee4ec51a2906 · outbound

This paper cites This shows that our approach can effectively handle label noise.

Adversarial Mixup Unlearning This shows that our approach can effectively handle label noise

Reference 11

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T18:45:26.709627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9c92db68-aee6-4610-a490-13e6ac9e7cea · outbound

This paper cites This comparison is performed using two metrics: training accuracy on the forgotten data (Trainf ) and testing accuracy.

Adversarial Mixup Unlearning This comparison is performed using two metrics: training accuracy on the forgotten data (Trainf ) and testing accuracy

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:45:26.672834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7d787efa-22c5-4680-9faf-82f4dc482c04 · outbound

This paper cites vit-b16-224-in21k.

Adversarial Mixup Unlearning vit-b16-224-in21k

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:45:26.653737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a5fe4b99-1d9c-4989-9d22-32c1a81f92ae · outbound

This paper cites Towards unbounded machine unlearning.

Adversarial Mixup Unlearning Towards unbounded machine unlearning

Reference 2009

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:45:26.858019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1dfb4ff7-77eb-408a-b4c3-3a820e51257a · outbound

This paper cites A Survey on Mixup Augmentations and Beyond.

Adversarial Mixup Unlearning A Survey on Mixup Augmentations and Beyond

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T18:45:26.501429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 095a4102-4099-4567-9ab1-ca6cf94216dc · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

Adversarial Mixup Unlearning Reading digits in natural images with unsupervised feature learning

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:45:26.822698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f1408678-963b-44af-b94b-33d9cc98f309 · outbound

This paper cites Convolutional networks for images, speech, and time series.

Adversarial Mixup Unlearning Convolutional networks for images, speech, and time series

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:45:26.841910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a1d383c5-4770-4e64-b251-4a49a677f763 · outbound

This paper cites Compared to Retraining, which requires a complete model retraining and is thus highly time- consuming, our method demonstrates significant superiority in efficiency.

Adversarial Mixup Unlearning Compared to Retraining, which requires a complete model retraining and is thus highly time- consuming, our method demonstrates significant superiority in efficiency

Reference 3090

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:45:26.690269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.