Paper Citation Record · LEDGER

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective

As of 10 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2507.06552.

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

pith.paper-citation-record.v1
2507.06552 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:13:37.869556Z

measured 38 of 38 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

38 of 38 outbound references displayed

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verified fuzzy27
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External citation measurements

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Outbound references

Observation c70f844f-b35e-42d6-af84-c4fcf7e68ed9 · outbound

This paper cites f-domain adversarial learning: Theory and algorithms.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective f-domain adversarial learning: Theory and algorithms

Reference 1

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This paper cites A model of inductive bias learning.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective A model of inductive bias learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:43.433760Z

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Observation e40ec06f-16c0-4911-a51d-e14f5964acc6 · outbound

This paper cites On the hardness of domain adaptation and the utility of unlabeled target samples.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective On the hardness of domain adaptation and the utility of unlabeled target samples

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:43.289599Z

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Observation 6d9028f7-b7f0-46ad-9f71-10336461ef05 · outbound

This paper cites Domain adaptation--can quantity compensate for quality? Annals of Mathematics and Artificial Intelligence, 70: 0 185--202, 2014.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Domain adaptation--can quantity compensate for quality? Annals of Mathematics and Artificial Intelligence, 70: 0 185--202, 2014

Reference 4

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Observation e4092aa2-12e5-45aa-88cc-68afa273875d · outbound

This paper cites Analysis of representations for domain adaptation.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Analysis of representations for domain adaptation

Reference 5

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Observation 67dd3507-bcfb-401d-a230-183586a31a45 · outbound

This paper cites A theory of learning from different domains.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective A theory of learning from different domains

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:43.034751Z

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Observation f12033cd-2300-4209-b514-aecf50efb7ef · outbound

This paper cites Learning bounds for domain adaptation.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Learning bounds for domain adaptation

Reference 7

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Observation e67010d0-42a6-4b6d-bf17-95201146a2de · outbound

This paper cites Learning bounds for importance weighting.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Learning bounds for importance weighting

Reference 8

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Observation d2c7b4c9-00d4-46f8-9376-01dd35f21655 · outbound

This paper cites Elements of information theory.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Elements of information theory

Reference 9

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Observation 927f3974-77eb-4054-a256-badc7ea9038d · outbound

This paper cites Eine information’s theoretische ungleichung und ihre anwendung auf den beweis der ergodizitat von markoschen ketten, magyar tud.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Eine information’s theoretische ungleichung und ihre anwendung auf den beweis der ergodizitat von markoschen ketten, magyar tud

Reference 10

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Observation f59e820a-1f25-43e2-b586-bbe0357e808a · outbound

This paper cites The value of out-of-distribution data.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective The value of out-of-distribution data

Reference 11

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Observation b31d7916-fadf-4c90-a938-3096736a1350 · outbound

This paper cites Class notes for course 6.574: Transmission of information.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Class notes for course 6.574: Transmission of information

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:42.265495Z

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Observation ba5b2c5d-0ca7-4d4e-8e67-39a08b9efbec · outbound

This paper cites Unsupervised domain adaptation by backpropagation.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Unsupervised domain adaptation by backpropagation

Reference 13

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Observation c5ac5a2f-ad8f-46cb-83ca-7b663f728afa · outbound

This paper cites Ghosh, and Aad W.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Ghosh, and Aad W

Reference 14

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Observation dfc33a29-55c7-46d8-bfd8-db2ad85c929b · outbound

This paper cites On the value of target data in transfer learning.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective On the value of target data in transfer learning

Reference 15

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Observation 1cb3fb59-0ee0-48ce-a23d-1b878f95777c · outbound

This paper cites Algorithms and theory for multiple-source adaptation.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Algorithms and theory for multiple-source adaptation

Reference 16

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Observation a0d4d770-fa09-4140-ad77-4d819660e714 · outbound

This paper cites Correcting sample selection bias by unlabeled data.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Correcting sample selection bias by unlabeled data

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:41.527408Z

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Observation e40827e3-5dfa-4206-9a9c-6772d763651e · outbound

This paper cites Unsupervised domain adaptation based on source-guided discrepancy.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Unsupervised domain adaptation based on source-guided discrepancy

Reference 18

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Observation f498d22c-de05-4972-8dfd-6a73a6f39a7a · outbound

This paper cites Coupled generative adversarial networks.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Coupled generative adversarial networks

Reference 19

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Observation 60f80765-787f-4ec7-8534-122daabeaf5d · outbound

This paper cites Domain adaptation: Learning bounds and algorithms.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Domain adaptation: Learning bounds and algorithms

Reference 20

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Observation a857e9e3-52b1-4f17-809a-56bda5ea20a0 · outbound

This paper cites Multiple Source Adaptation and the Renyi Divergence.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Multiple Source Adaptation and the Renyi Divergence

Reference 21

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Observation ac252a57-a5fe-4828-9a29-6cdb9a45ecb4 · outbound

This paper cites Some pac-bayesian theorems.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Some pac-bayesian theorems

Reference 22

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Observation 6f880ff2-045d-48d1-9e92-b8a08f57cd02 · outbound

This paper cites New analysis and algorithm for learning with drifting distributions.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective New analysis and algorithm for learning with drifting distributions

Reference 23

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Observation d1a6b658-7067-418e-89f3-38c5b267efa5 · outbound

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On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective KL Guided Domain Adaptation

Reference 24

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Observation 50debcdb-63c2-4a60-98e6-a15f0599675c · outbound

This paper cites Statistical aspects of wasserstein distances.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Statistical aspects of wasserstein distances

Reference 25

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Observation 2595e68e-0fa1-44d1-b457-9185dddce85f · outbound

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On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective A pac analysis of a bayesian estimator

Reference 26

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Observation 57752e0d-885d-4e2a-a19c-37e12534b151 · outbound

This paper cites Wasserstein distance guided representation learning for domain adaptation.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Wasserstein distance guided representation learning for domain adaptation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:40.089042Z

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Observation d1851e09-14ab-4332-a2ae-61817d419d45 · outbound

This paper cites Improving predictive inference under covariate shift by weighting the log-likelihood function.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Improving predictive inference under covariate shift by weighting the log-likelihood function

Reference 28

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Observation d98a9308-1361-4163-94b3-7c9e36c7f674 · outbound

This paper cites A novel domain adaptation theory with jensen--shannon divergence.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective A novel domain adaptation theory with jensen--shannon divergence

Reference 29

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This paper cites Bridging the gap between f-gans and wasserstein gans.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Bridging the gap between f-gans and wasserstein gans

Reference 30

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On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective A theory of the learnable

Reference 31

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Observation 3d34f2ac-8279-4c08-b4d6-6bd2e4296b33 · outbound

This paper cites Information-Theoretic Analysis of Unsupervised Domain Adaptation.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Information-Theoretic Analysis of Unsupervised Domain Adaptation

Reference 32

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This paper cites On f -divergence principled domain adaptation: An improved framework.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective On f -divergence principled domain adaptation: An improved framework

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:38.676071Z

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This paper cites Bridging theory and algorithm for domain adaptation.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Bridging theory and algorithm for domain adaptation

Reference 34

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This paper cites write newline.

On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective write newline

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On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective @esa (Ref

Reference 36

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On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Unresolved cited work

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On the Hardness of Unsupervised Domain Adaptation: Optimal Learners and Information-Theoretic Perspective Unresolved cited work

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Pith citing papers

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