Paper Citation Record · LEDGER

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning

As of 10 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 1 inbound Pith citation observation for arXiv:2506.20893.

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

pith.paper-citation-record.v1
2506.20893 v5

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:45:50.113649Z

measured 17 of 17 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-08T21:30:38.122700Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-08T21:35:37.706190Z

Reference resolution

16 of 16 outbound references displayed

verified exact2
verified fuzzy7
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e34a92ea-5f4f-4dd7-afe6-7c651942d695 · outbound

This paper cites Machine Unlearning via Null Space Calibration.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning Machine Unlearning via Null Space Calibration

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:45:50.475708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 29b1dad4-7fff-484b-a9b3-2912934d887d · outbound

This paper cites Camu: Disentangling causal effects in deep model unlearning.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning Camu: Disentangling causal effects in deep model unlearning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:51.267001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ab94e9e7-25c3-45a3-9cd7-15a23c8c866f · outbound

This paper cites Machine Unlearning of Features and Labels.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning Machine Unlearning of Features and Labels

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.813721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9983c7f4-9a7a-4d1c-a0c6-3dbedcc64bbf · outbound

This paper cites [2024], and SCRUB Kurmanji et al.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning [2024], and SCRUB Kurmanji et al

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:50.860477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4e825531-a895-41da-91e4-c21f88c485f6 · outbound

This paper cites To evaluate unlearning on a more challenging benchmark, we also apply our method to theTiny-ImageNet- 200dataset using a ResNet-18 backbone.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning To evaluate unlearning on a more challenging benchmark, we also apply our method to theTiny-ImageNet- 200dataset using a ResNet-18 backbone

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:50.713908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1f2e0eeb-5e2b-45b3-a695-4efb353b5769 · outbound

This paper cites Towards unbounded machine unlearning.Advances in neural information processing systems, 36:1957–1987,.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning Towards unbounded machine unlearning.Advances in neural information processing systems, 36:1957–1987,

Reference 2009

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:51.421823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d519c48f-3511-4207-b15b-b4293827d023 · outbound

This paper cites Not all wrong is bad: Using adversarial examples for unlearning.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning Not all wrong is bad: Using adversarial examples for unlearning

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.220090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ee7ee18-5b50-4d08-812f-de282beb976b · outbound

This paper cites an unresolved cited work.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning Unresolved cited work

Reference 2014

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:45:51.011122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d05297c0-966a-41a2-ae23-f5dd1863519c · outbound

This paper cites Partially Blinded Unlearning: Class Unlearning for Deep Networks a Bayesian Perspective.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning Partially Blinded Unlearning: Class Unlearning for Deep Networks a Bayesian Perspective

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.557256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6598abb8-a6bb-46f3-9a8e-a03d8a9c7369 · outbound

This paper cites Approximate data deletion from machine learning models.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning Approximate data deletion from machine learning models

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:51.645244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation feda15c8-2131-4de5-931e-3b84621e0848 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.743696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8c176b3-fc97-43f9-bc42-912bc6db502c · outbound

This paper cites Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.309697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6086e074-f97d-4ea2-ab00-c423dd929f31 · outbound

This paper cites Membership inferenceattacksfromfirstprinciples.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning Membership inferenceattacksfromfirstprinciples

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:51.886526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2d3baf52-5293-4b1e-81ed-2d3458991ae3 · outbound

This paper cites SALUN Fan et al.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning SALUN Fan et al

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:45:51.162268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 16dd18a8-4833-4fb3-997f-97e634420f73 · outbound

This paper cites Zero-shot Class Unlearning via Layer-wise Relevance Analysis and Neuronal Path Perturbation.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning Zero-shot Class Unlearning via Layer-wise Relevance Analysis and Neuronal Path Perturbation

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:45:50.595699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation be70cd17-3e9a-4b70-a910-6bf5a61fe582 · outbound

This paper cites SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation.

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:49.269250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation d5dc7cbd-7d25-4a08-a467-b036f3cea758 · inbound

Auditing of Unlearning Algorithms cites this paper.

Auditing of Unlearning Algorithms On the Necessity of Output Distribution Reweighting for Effective Class Unlearning

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-14T01:19:53.864690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.