Paper Citation Record · LEDGER

Addressing Label Shift in Distributed Learning via Entropy Regularization

As of 22 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2502.02544.

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

pith.paper-citation-record.v1
2502.02544 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:54:05.112035Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

28 of 28 outbound references displayed

verified exact0
verified fuzzy10
unresolved16
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8f88208b-6469-4f53-9de2-e2001c149ea8 · outbound

This paper cites an unresolved cited work.

Addressing Label Shift in Distributed Learning via Entropy Regularization Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:54:05.471285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a19da743-db2a-4add-978c-4062eda8db01 · outbound

This paper cites an unresolved cited work.

Addressing Label Shift in Distributed Learning via Entropy Regularization Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:54:05.434556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cfbfa751-3700-443c-a758-9368ff7fa30e · outbound

This paper cites FL Games: A federated learning framework for distribution shifts.

Addressing Label Shift in Distributed Learning via Entropy Regularization FL Games: A federated learning framework for distribution shifts

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T11:54:04.999578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 929a0750-1f2b-48ec-a75b-c51b1f1273bd · outbound

This paper cites Theorem F .3(Oracle Complexity of Proximal Operator for Composite Optimization).

Addressing Label Shift in Distributed Learning via Entropy Regularization Theorem F .3(Oracle Complexity of Proximal Operator for Composite Optimization)

Reference 4

Resolution: malformed identifier; raw_fallback, observed 2026-08-09T11:54:05.412079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f893f94f-002b-47be-9d90-cb17057e0107 · outbound

This paper cites You-Wei Luo and Chuan-Xian Ren.

Addressing Label Shift in Distributed Learning via Entropy Regularization You-Wei Luo and Chuan-Xian Ren

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T11:54:05.014673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9817278d-b43e-465e-a39e-3a1f995ffe98 · outbound

This paper cites an unresolved cited work.

Addressing Label Shift in Distributed Learning via Entropy Regularization Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:54:05.596655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e56dbdf3-2285-413d-80d5-aa4123fe81ba · outbound

This paper cites Flexifed: Personalized federated learning for edge clients with heterogeneous model architectures.

Addressing Label Shift in Distributed Learning via Entropy Regularization Flexifed: Personalized federated learning for edge clients with heterogeneous model architectures

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:54:05.585213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 253a623d-4ff4-4e76-9ccc-f4daf89718fb · outbound

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

Addressing Label Shift in Distributed Learning via Entropy Regularization Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T11:54:05.041039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b36f6f5-229e-45ca-9bee-57a866f2f399 · outbound

This paper cites • Additional details of prior work of BBSE and MLLS are in Appendix B.

Addressing Label Shift in Distributed Learning via Entropy Regularization • Additional details of prior work of BBSE and MLLS are in Appendix B

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:54:05.573962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1643f80e-2bb9-48df-be01-0a985d5d7b2c · outbound

This paper cites In this section, we overview complete related work.

Addressing Label Shift in Distributed Learning via Entropy Regularization In this section, we overview complete related work

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:54:05.561209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7644966d-9f87-4168-a100-3838d6a76db1 · outbound

This paper cites an unresolved cited work.

Addressing Label Shift in Distributed Learning via Entropy Regularization Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:54:05.550362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 49ab223d-1fa3-4366-b3b1-fb41a6bcb99b · outbound

This paper cites This approach is straightforward and has been proven consistent, even when the predictor is not calibrated.

Addressing Label Shift in Distributed Learning via Entropy Regularization This approach is straightforward and has been proven consistent, even when the predictor is not calibrated

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:54:05.539825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c9ec9a2e-b0db-444d-a2fa-b7815121c3e5 · outbound

This paper cites It also provides a consistency guarantee with a canonically calibrated predictor.

Addressing Label Shift in Distributed Learning via Entropy Regularization It also provides a consistency guarantee with a canonically calibrated predictor

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:54:05.528752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0647a53e-672b-4220-a0c9-be29626e25d3 · outbound

This paper cites Concerning IPMs, while MMD is reliant on a kernel function, it can suffer from the curse of dimensionality when faced with high-dimensional data.

Addressing Label Shift in Distributed Learning via Entropy Regularization Concerning IPMs, while MMD is reliant on a kernel function, it can suffer from the curse of dimensionality when faced with high-dimensional data

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:54:05.494863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0a4f6ab6-2335-45f1-b522-580a749a2a1a · outbound

This paper cites an unresolved cited work.

Addressing Label Shift in Distributed Learning via Entropy Regularization Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:54:05.459212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 82cf0f34-9be3-4f4b-9fc9-3c09194a415d · outbound

This paper cites an unresolved cited work.

Addressing Label Shift in Distributed Learning via Entropy Regularization Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:54:05.446060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fdb62e11-f7bd-46a0-8892-e88ba49b6907 · outbound

This paper cites an unresolved cited work.

Addressing Label Shift in Distributed Learning via Entropy Regularization Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:54:05.423620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 34d1785b-6b15-45da-b2d2-bc8c3f713750 · outbound

This paper cites an unresolved cited work.

Addressing Label Shift in Distributed Learning via Entropy Regularization Unresolved cited work

Reference 1951

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:54:05.506108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation af57cee1-129f-44a0-b713-f0913518ab08 · outbound

This paper cites Federated optimization in heterogeneous networks.

Addressing Label Shift in Distributed Learning via Entropy Regularization Federated optimization in heterogeneous networks

Reference 1998

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:54:05.620870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4ae42b5b-19ce-4f3d-9d18-3d2bcf631ac4 · outbound

This paper cites Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation.

Addressing Label Shift in Distributed Learning via Entropy Regularization Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation

Reference 2000

Resolution: unresolved; no resolver link, observed 2026-08-09T11:54:05.027463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57e6ac96-db2e-4b93-8059-3c826f1d755f · outbound

This paper cites In contrast, f- divergences, such as KL-divergence (Kullback & Leibler,.

Addressing Label Shift in Distributed Learning via Entropy Regularization In contrast, f- divergences, such as KL-divergence (Kullback & Leibler,

Reference 2012

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:54:05.517587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3b4f42f3-aa08-46c8-b0ce-fa817f5277f2 · outbound

This paper cites an unresolved cited work.

Addressing Label Shift in Distributed Learning via Entropy Regularization Unresolved cited work

Reference 2017

Resolution: malformed identifier; raw_fallback, observed 2026-08-09T11:54:05.399841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1eedd204-8534-4d09-b00b-0232f1af60ee · outbound

This paper cites log( mX c=1 ptr(z, y= c)rc) # = Ete.

Addressing Label Shift in Distributed Learning via Entropy Regularization log( mX c=1 ptr(z, y= c)rc) # = Ete

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:54:05.483047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b2efed69-d92c-4bfd-ba8f-1649f39c069c · outbound

This paper cites Mitigating Data Heterogeneity in Federated Learning with Data Augmentation.

Addressing Label Shift in Distributed Learning via Entropy Regularization Mitigating Data Heterogeneity in Federated Learning with Data Augmentation

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-09T11:54:04.990707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62087d14-e25a-44ce-a91f-dd9aa628fa30 · outbound

This paper cites Domain Adaptation under Open Set Label Shift.

Addressing Label Shift in Distributed Learning via Entropy Regularization Domain Adaptation under Open Set Label Shift

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-09T11:54:04.995935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87364f92-7fb5-42e9-8345-e065c3d0c20c · outbound

This paper cites Do CIFAR-10 Classifiers Generalize to CIFAR-10?.

Addressing Label Shift in Distributed Learning via Entropy Regularization Do CIFAR-10 Classifiers Generalize to CIFAR-10?

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T11:54:05.022196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 265b937a-2f77-4aee-9b19-b5dc7080823d · outbound

This paper cites Gradma: A gradient-memory-based ac- celerated federated learning with alleviated catastrophic forgetting.

Addressing Label Shift in Distributed Learning via Entropy Regularization Gradma: A gradient-memory-based ac- celerated federated learning with alleviated catastrophic forgetting

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:54:05.609269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a8023d9b-986c-4659-a7a6-bf591b5376d9 · outbound

This paper cites Regularizing Neural Networks by Penalizing Confident Output Distributions.

Addressing Label Shift in Distributed Learning via Entropy Regularization Regularizing Neural Networks by Penalizing Confident Output Distributions

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T11:54:05.018159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.