Paper Citation Record · LEDGER

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension

As of 20 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.16704.

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

pith.paper-citation-record.v1
2506.16704 v3

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:31:22.132607Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

40 of 40 outbound references displayed

verified exact1
verified fuzzy24
unresolved15
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6f7d5a0a-4924-483e-9ddb-67b7c6474d5a · outbound

This paper cites Metalearning with very few samples per task.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Metalearning with very few samples per task

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.684612Z

Source-reported events for the cited work

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Observation 77b0b6fb-6342-479b-a694-b6f8507eb733 · outbound

This paper cites A theory of PAC learnability of partial concept classes.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension A theory of PAC learnability of partial concept classes

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.673871Z

Source-reported events for the cited work

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Observation 0d39e71f-40f0-4be9-bf21-f7b557d47547 · outbound

This paper cites On the ERM Principle in Meta-Learning.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension On the ERM Principle in Meta-Learning

Reference 3

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Observation 65661944-112f-48df-95e1-1a027140c33b · outbound

This paper cites Invariant Risk Minimization.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Invariant Risk Minimization

Reference 4

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Observation cd9d9140-cd61-4a93-8d5d-3c00dfbf5421 · outbound

This paper cites Open problem: The sample complexity of multi-distribution learning for VC classes.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Open problem: The sample complexity of multi-distribution learning for VC classes

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.661942Z

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Observation 0361c702-9847-45e8-9af4-c22e48e0b738 · outbound

This paper cites A theory of learning from different domains.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension A theory of learning from different domains

Reference 6

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Source-reported events for the cited work

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Observation 21063f15-7458-43eb-81b9-f296a0c8d565 · outbound

This paper cites Generalizing from several related classification tasks to a new unlabeled sample.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Generalizing from several related classification tasks to a new unlabeled sample

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.642922Z

Source-reported events for the cited work

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Observation 57cf13d5-5cb9-4d30-81ca-875759fde0d9 · outbound

This paper cites Collaborative PAC learning.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Collaborative PAC learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.632066Z

Source-reported events for the cited work

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Observation bce4101e-1333-4f97-8255-5f3f23dfe905 · outbound

This paper cites Tight bounds for collaborative PAC learning via multiplicative weights.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Tight bounds for collaborative PAC learning via multiplicative weights

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.621415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 89e27808-0a2c-4db2-82f2-e11a4cadead8 · outbound

This paper cites Exploiting shared representations for personalized federated learning.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Exploiting shared representations for personalized federated learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T19:31:22.014612Z

Source-reported events for the cited work

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Observation c8a69c7b-e79d-4982-9547-40c78cd7611a · outbound

This paper cites Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T19:31:22.019061Z

Source-reported events for the cited work

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Observation bdd8d86f-27d2-4a19-be66-5d5d50d53cad · outbound

This paper cites Kakade, Jason D.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Kakade, Jason D

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.602326Z

Source-reported events for the cited work

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Observation d2edec32-a037-42f2-8a7e-ffa341816094 · outbound

This paper cites Subspace recovery from heterogeneous data with non-isotropic noise.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Subspace recovery from heterogeneous data with non-isotropic noise

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.591460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a3bb2290-e7e6-4404-93b5-83b24b4143a0 · outbound

This paper cites Learn to expect the unexpected: Probably approximately correct domain generalization.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Learn to expect the unexpected: Probably approximately correct domain generalization

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.579663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e2ba0f1b-db3a-4da3-bd1e-fb76e15ce423 · outbound

This paper cites On-demand sampling: Learning optimally from multiple distributions.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension On-demand sampling: Learning optimally from multiple distributions

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.568566Z

Source-reported events for the cited work

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Observation 357bd556-7550-430f-93b2-28a81ed7ba49 · outbound

This paper cites Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes

Reference 16

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Observation 387de4bc-b585-4d04-942f-8eb7be5e08f0 · outbound

This paper cites Metric entropy duality and the sample complexity of outcome indistinguishability.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Metric entropy duality and the sample complexity of outcome indistinguishability

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.550281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a762da6a-da8e-45ea-9ff5-387b5b73d716 · outbound

This paper cites Efficient distribution-free learning of probabilistic concepts.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Efficient distribution-free learning of probabilistic concepts

Reference 18

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Source-reported events for the cited work

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Observation 5d47a670-8e73-4a6e-97fe-fb440f639f3a · outbound

This paper cites Universal adaptability: Target-independent inference that competes with propensity scoring.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Universal adaptability: Target-independent inference that competes with propensity scoring

Reference 19

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Source-reported events for the cited work

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Observation bc965cde-47d2-4d6a-848a-3ded635f8d49 · outbound

This paper cites Meta-learning for mixed linear regression.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Meta-learning for mixed linear regression

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.526232Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0892ee3e-5474-4206-b841-f137696f464e · outbound

This paper cites Derandomizing multi-distribution learning.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Derandomizing multi-distribution learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.513976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 731e54f0-ef78-42a4-ac69-0bf805464f2f · outbound

This paper cites Learning adversarially fair and transferable representations.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Learning adversarially fair and transferable representations

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.503081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fd5e296b-b52c-465e-8004-53466dfe4bb6 · outbound

This paper cites The benefit of multitask representation learning.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension The benefit of multitask representation learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.491347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0f77eb8a-e673-4a48-a3e2-dde93f4cfc4b · outbound

This paper cites Agnostic federated learning.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Agnostic federated learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.479521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2773172b-ebac-4e85-a691-980c0e3059ea · outbound

This paper cites Transformation-invariant learning and theoretical guarantees for OOD generalization.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Transformation-invariant learning and theoretical guarantees for OOD generalization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.468617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e0780a8e-efed-47f0-bf5d-89353d4d2962 · outbound

This paper cites Domain generalization via invariant feature representation.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Domain generalization via invariant feature representation

Reference 26

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Source-reported events for the cited work

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Observation fe304e84-260b-4383-b51a-cf804ac6c609 · outbound

This paper cites Improved algorithms for collaborative PAC learning.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Improved algorithms for collaborative PAC learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.450553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5f10c8a6-e845-4c25-b71f-8a20887578e5 · outbound

This paper cites The sample complexity of multi-distribution learning.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension The sample complexity of multi-distribution learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.439410Z

Source-reported events for the cited work

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Observation c6a894fc-6cd3-4510-b498-f6142a96fb72 · outbound

This paper cites On the density of families of sets.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension On the density of families of sets

Reference 29

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Source-reported events for the cited work

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This paper cites A theory of PAC learnability under transformation invariances.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension A theory of PAC learnability under transformation invariances

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.428242Z

Source-reported events for the cited work

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Observation 52af4c10-34b3-4873-bf11-ffeb54e7e8da · outbound

This paper cites A combinatorial problem; stability and order for models and theories in infinitary languages.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension A combinatorial problem; stability and order for models and theories in infinitary languages

Reference 31

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Source-reported events for the cited work

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Observation f2212a25-6ac4-4e0c-b79d-11306ac29991 · outbound

This paper cites Sample Efficient Linear Meta-Learning by Alternating Minimization.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Sample Efficient Linear Meta-Learning by Alternating Minimization

Reference 32

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Observation c647a732-c254-4e80-b031-638e09f12ca6 · outbound

This paper cites On the theory of transfer learning: The importance of task diversity.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension On the theory of transfer learning: The importance of task diversity

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.410773Z

Source-reported events for the cited work

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Observation fe5b433d-cb95-4b7b-ae62-295c64392bc4 · outbound

This paper cites Provable meta-learning of linear representations.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Provable meta-learning of linear representations

Reference 34

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Source-reported events for the cited work

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Observation ab8048da-48ce-478e-9673-a617f10687b4 · outbound

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How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Unresolved cited work

Reference 35

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Source-reported events for the cited work

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Observation d6a33c65-8db7-4d99-88f6-f8c593348ee7 · outbound

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How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Unresolved cited work

Reference 36

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Observation 4cdec297-b7c3-4273-8a5f-05f4a283afbf · outbound

This paper cites High-dimensional probability: An introduction with applications in data science, volume 47.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension High-dimensional probability: An introduction with applications in data science, volume 47

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T19:31:22.122304Z

Source-reported events for the cited work

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Observation edb3390a-577d-4e3b-936b-77e464ce4715 · outbound

This paper cites Generalizing to unseen domains: A survey on domain generalization.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Generalizing to unseen domains: A survey on domain generalization

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.386480Z

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Observation 95c057ff-ded2-46d6-b04d-713b5d5cf6cc · outbound

This paper cites Optimal multi-distribution learning.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Optimal multi-distribution learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.373297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7ebc65fe-a0a2-4c02-8085-c01f9f8f0e31 · outbound

This paper cites Domain generalization: A survey.

How Many Domains Suffice for Domain Generalization? A Tight Characterization via the Domain Shattering Dimension Domain generalization: A survey

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:22.360358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.