Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:45:50.113649Z
Paper Citation Record · LEDGER
On the Necessity of Output Distribution Reweighting for Effective Class Unlearning
As of 15 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-08T21:30:38.122700Z
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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+00:00.