A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation | Paper Citation Record

Observation c51b693b-600a-429f-937a-b28108ae060b · outbound

This paper cites A review of domain adaptation without target labels,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation A review of domain adaptation without target labels,

Reference 1

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Observation b5c3db29-cea2-418c-9a16-57319a3e301a · outbound

This paper cites Regularized learning for domain adaptation under label shifts,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Regularized learning for domain adaptation under label shifts,

Reference 2

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This paper cites Domain adaptation with conditional distribution matching and generalized label shift,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Domain adaptation with conditional distribution matching and generalized label shift,

Reference 3

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Observation 7d1c8a08-f30e-4926-91a8-e75532da2052 · outbound

This paper cites Domain adaptation: Challenges, methods, datasets, and applications,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Domain adaptation: Challenges, methods, datasets, and applications,

Reference 4

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This paper cites Cor- recting sample selection bias by unlabeled data,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Cor- recting sample selection bias by unlabeled data,

Reference 5

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Observation 96c21987-f749-42e8-8ac4-345a8d5e6b04 · outbound

This paper cites A two-stage weighting framework for multi-source domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation A two-stage weighting framework for multi-source domain adaptation,

Reference 6

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Observation f95f1dad-5711-4fde-bf3d-6873b2194bb7 · outbound

This paper cites Frustratingly easy domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Frustratingly easy domain adaptation,

Reference 7

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Observation 46056a4a-fe91-412f-a644-1254b7f62f7b · outbound

This paper cites Co-regularization based semi-supervised domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Co-regularization based semi-supervised domain adaptation,

Reference 8

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Observation 4633b9d2-4430-41b7-9e52-22cce9c9e451 · outbound

This paper cites Learning with augmented features for hetero- geneous domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Learning with augmented features for hetero- geneous domain adaptation,

Reference 9

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Observation fd6ea1f1-e15c-4fd6-b264-da059e9d09ca · outbound

This paper cites Unsuper- vised domain adaptation by domain invariant projection,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Unsuper- vised domain adaptation by domain invariant projection,

Reference 10

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Observation 05ef2362-fe1a-49c5-9a5f-cdd1146ed486 · outbound

This paper cites Domain adaptation via transfer component analysis,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Domain adaptation via transfer component analysis,

Reference 11

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Observation ef0ed8a3-a8d7-4fd8-a14d-a6e2dc12c48b · outbound

This paper cites Semi-supervised domain adaptation with subspace learning for visual recognition,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Semi-supervised domain adaptation with subspace learning for visual recognition,

Reference 12

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Observation 2abef484-797c-4d9a-b18b-8cd478ab80a3 · outbound

This paper cites Deep visual domain adaptation: A survey,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Deep visual domain adaptation: A survey,

Reference 13

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Observation 950b1aa0-e5c0-4cdd-9735-ff1d250913d4 · outbound

This paper cites Learning transferable features with deep adaptation networks,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Learning transferable features with deep adaptation networks,

Reference 14

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Observation 80209368-1e3e-47f3-9fdd-8399299ebcf8 · outbound

This paper cites Deep Domain Confusion: Maximizing for Domain Invariance.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Deep Domain Confusion: Maximizing for Domain Invariance

Reference 15

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This paper cites Domain adaptive neural networks for object recognition,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Domain adaptive neural networks for object recognition,

Reference 16

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Observation ae34593d-2ac7-4abf-97e4-53f5fb692c63 · outbound

This paper cites Multirepresentation dynamic adaptive network for cross-domain rolling bearing fault diagnosis in complex scenarios,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Multirepresentation dynamic adaptive network for cross-domain rolling bearing fault diagnosis in complex scenarios,

Reference 17

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This paper cites Information maximizing adaptation network with label distribu- tion priors for unsupervised domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Information maximizing adaptation network with label distribu- tion priors for unsupervised domain adaptation,

Reference 18

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Observation 8c8bb27e-ba05-4c1d-a60e-8e3b6ce3f702 · outbound

This paper cites Meta domain adaptation approach for multi-domain ranking,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Meta domain adaptation approach for multi-domain ranking,

Reference 19

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This paper cites Point-to-set metric-gated mixture of experts for multisource do- main adaptation fault diagnosis,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Point-to-set metric-gated mixture of experts for multisource do- main adaptation fault diagnosis,

Reference 20

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This paper cites Domain-adversarial training of neural networks,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Domain-adversarial training of neural networks,

Reference 21

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Observation 07ffda7e-2454-40b7-a153-95f29f3e58f6 · outbound

This paper cites Adversarial discriminative domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Adversarial discriminative domain adaptation,

Reference 22

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Observation cc4eaf55-47d6-4d03-8786-a41157d40a81 · outbound

This paper cites Discriminative adversarial domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Discriminative adversarial domain adaptation,

Reference 23

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This paper cites A survey on adversarial domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation A survey on adversarial domain adaptation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.582815Z

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This paper cites Deep reconstruction-classification networks for unsupervised domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Deep reconstruction-classification networks for unsupervised domain adaptation,

Reference 25

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This paper cites Domain separation networks,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Domain separation networks,

Reference 26

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This paper cites An unsupervised adversarial domain adaptation based on variational auto-encoder,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation An unsupervised adversarial domain adaptation based on variational auto-encoder,

Reference 27

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This paper cites Deep CORAL: correlation alignment for deep domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Deep CORAL: correlation alignment for deep domain adaptation,

Reference 28

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This paper cites Optimal transport for domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Optimal transport for domain adaptation,

Reference 29

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This paper cites Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation,

Reference 30

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Observation 93315946-2849-4a89-8a1b-468ba2a82ee8 · outbound

This paper cites Theoretical guarantees for domain adap- tation with hierarchical optimal transport,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Theoretical guarantees for domain adap- tation with hierarchical optimal transport,

Reference 31

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This paper cites A survey on domain adaptation theory: learning bounds and theoretical guarantees.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation A survey on domain adaptation theory: learning bounds and theoretical guarantees

Reference 32

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This paper cites Analysis of representations for domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Analysis of representations for domain adaptation,

Reference 33

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Observation 17886b53-9f2e-4acd-be79-d5d10aefdf74 · outbound

This paper cites Domain adaptation: Learning bounds and algorithms,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Domain adaptation: Learning bounds and algorithms,

Reference 34

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This paper cites Bridging theory and algorithm for domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Bridging theory and algorithm for domain adaptation,

Reference 35

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Observation 7c2fefea-7c7e-4f38-b1cc-5785ec471db6 · outbound

This paper cites Margin-aware adversarial domain adap- tation with optimal transport,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Margin-aware adversarial domain adap- tation with optimal transport,

Reference 36

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This paper cites On f-divergence principled domain adaptation: An im- proved framework,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation On f-divergence principled domain adaptation: An im- proved framework,

Reference 37

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This paper cites Multi-class heterogeneous domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Multi-class heterogeneous domain adaptation,

Reference 38

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This paper cites Semi-supervised heterogeneous domain adaptation: Theory and algorithms,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Semi-supervised heterogeneous domain adaptation: Theory and algorithms,

Reference 39

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Observation d5b1c872-5e36-4432-a3ff-e872a8919aba · outbound

This paper cites Generalization bounds for transfer learning under model shift,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Generalization bounds for transfer learning under model shift,

Reference 40

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Observation a86718d1-8d1b-4660-824f-26af6b08114f · outbound

This paper cites A theoretical framework for deep transfer learning,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation A theoretical framework for deep transfer learning,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.461382Z

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Observation 093be771-ed55-4079-84f8-fd6d0be9e084 · outbound

This paper cites Risk bounds for transferring representations with and without fine-tuning,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Risk bounds for transferring representations with and without fine-tuning,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.453953Z

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This paper cites Deep Transfer Learning: Model Framework and Error Analysis.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Deep Transfer Learning: Model Framework and Error Analysis

Reference 43

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A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Unresolved cited work

Reference 44

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Observation 0789ae3a-dde9-4a22-9f1f-7d4e817a173a · outbound

This paper cites Norm-based capacity control in neural networks,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Norm-based capacity control in neural networks,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.438036Z

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Observation 4203207b-420c-49ba-ae5c-53dc748703e6 · outbound

This paper cites Data-dependent sample complexity of deep neural networks via Lipschitz augmentation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Data-dependent sample complexity of deep neural networks via Lipschitz augmentation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.430128Z

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Observation 4c0e7717-5469-4b6f-8999-bb085df49ec8 · outbound

This paper cites The sample complexity of one-hidden-layer neural networks,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation The sample complexity of one-hidden-layer neural networks,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.422224Z

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Observation 4bff2d88-4039-499a-8f58-88e869a90344 · outbound

This paper cites On the sample complexity of two-layer networks: Lipschitz vs. element-wise Lipschitz activation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation On the sample complexity of two-layer networks: Lipschitz vs. element-wise Lipschitz activation,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.414526Z

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Observation 3fa10e10-fee4-4497-b7e3-7de2cd2679b9 · outbound

This paper cites Generalization bounds for domain adaptation via domain transforma- tions,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Generalization bounds for domain adaptation via domain transforma- tions,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.406451Z

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This paper cites On the Mathematical Foundations of Learning,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation On the Mathematical Foundations of Learning,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.398597Z

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Observation 0a2b4361-ac62-4409-abb6-5c040eff5ff5 · outbound

This paper cites A kernel two-sample test,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation A kernel two-sample test,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.391109Z

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Observation 5c032da0-2dbf-4c01-8bcd-b945b1a78bd3 · outbound

This paper cites Dunford and J.T.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Dunford and J.T

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.382737Z

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 3d34f9e9-e9ae-49f9-976c-623be797fe82 · outbound

This paper cites Conditional adversarial domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Conditional adversarial domain adaptation,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.374815Z

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 aadb1ddf-426e-4707-8112-bf84eab36d3a · outbound

This paper cites Simultaneous deep transfer across domains and tasks,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Simultaneous deep transfer across domains and tasks,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.366812Z

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 50011fc1-5d6f-41e0-85d5-c9413342b069 · outbound

This paper cites A theory of learning from different domains,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation A theory of learning from different domains,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.359088Z

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 5ca07e26-8a11-4347-80ba-28e446b55441 · outbound

This paper cites On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-06T11:35:37.730626Z

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 d430416e-d71d-4243-8827-90371557c094 · outbound

This paper cites On generalization in moment- based domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation On generalization in moment- based domain adaptation,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.351191Z

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 66a2ea8f-4e15-4dff-abd0-efb3d9d4c56a · outbound

This paper cites Information-theoretic analysis of unsupervised domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Information-theoretic analysis of unsupervised domain adaptation,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.343405Z

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 619d0a10-baa0-40c0-a790-9edb9ccfc42d · outbound

This paper cites On the generalization for transfer learning: An information-theoretic analysis,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation On the generalization for transfer learning: An information-theoretic analysis,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.335339Z

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 88358869-d8f5-4737-b166-ed47888a7401 · outbound

This paper cites PAC-Bayesian domain adaptation bounds for multiclass learners,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation PAC-Bayesian domain adaptation bounds for multiclass learners,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.327453Z

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 e7d792c4-f7b4-4e1e-b2ce-73a25deb52b5 · outbound

This paper cites Gap minimization for knowledge sharing and transfer,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Gap minimization for knowledge sharing and transfer,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.319538Z

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 4a783f01-f3b3-401c-9dc6-b1d7a0ad7cfb · outbound

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

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation New analysis and algorithm for learning with drifting distributions,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.311838Z

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 ff334bf5-6179-4aa7-8187-04949bab72b0 · outbound

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

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation On the theory of transfer learning: The importance of task diversity,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.303982Z

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 0bbadffc-ff6b-443a-a847-a43858eebbe1 · outbound

This paper cites Deep learning: a statistical view- point,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Deep learning: a statistical view- point,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.295361Z

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 7359d902-70d6-41be-8795-a9561fe7e419 · outbound

This paper cites A PAC-bayesian approach to spectrally-normalized margin bounds for neural networks,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation A PAC-bayesian approach to spectrally-normalized margin bounds for neural networks,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.287944Z

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 2ea813fc-2f6d-4ef5-89be-bfe5d5a0341e · outbound

This paper cites Size-independent sample complexity of neural networks,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Size-independent sample complexity of neural networks,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.279819Z

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 e7e5bbf5-d83c-4615-b042-a745cb0db568 · outbound

This paper cites Spectrally-normalized margin bounds for neural networks,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Spectrally-normalized margin bounds for neural networks,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:38.110477Z

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 f86a8c9f-6ad7-40e1-8989-894f5d23b38b · outbound

This paper cites Nearly-tight VC-dimension bounds for piecewise linear neural networks,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Nearly-tight VC-dimension bounds for piecewise linear neural networks,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:37.928473Z

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 6f735422-959c-4334-9e62-cfa64c991e3c · outbound

This paper cites MIT-CBCL face recognition database,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation MIT-CBCL face recognition database,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:37.858573Z

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 8a6d36d9-55fa-44b9-90f8-ae132ab45bcf · outbound

This paper cites Unsupervised visual domain adaptation using subspace alignment,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Unsupervised visual domain adaptation using subspace alignment,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:37.833981Z

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 9ec33158-1fd5-4220-ae6d-c16faed902b5 · outbound

This paper cites Gradient-based learning applied to document recognition,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Gradient-based learning applied to document recognition,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:37.826454Z

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 02c5e012-6cb0-4899-8d3b-a1651396bb9f · outbound

This paper cites Unsupervised domain adaptation by backpropaga- tion,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Unsupervised domain adaptation by backpropaga- tion,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:37.818871Z

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 0a77ebb7-5683-4e1d-b269-c60966c8f8d3 · outbound

This paper cites An experimental study of the sample complexity of domain adaptation,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation An experimental study of the sample complexity of domain adaptation,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:37.811313Z

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 c8b3cda1-ff52-4716-9d3f-43d8718d76f5 · outbound

This paper cites Deep adaptation networks (DAN) in PyTorch,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Deep adaptation networks (DAN) in PyTorch,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:37.803244Z

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 d085ef85-120e-466b-9dd7-83cf7790f018 · outbound

This paper cites Dann py3,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Dann py3,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:37.794816Z

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 755f02fa-1e64-4473-be43-1b89ca5826d8 · outbound

This paper cites Exponential inequalities for sums of random vectors,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Exponential inequalities for sums of random vectors,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:37.786538Z

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 ca6d6f7c-d1d5-4983-9638-077b3cde0507 · outbound

This paper cites Reproducing Kernel Hilbert Spaces - Part III,.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Reproducing Kernel Hilbert Spaces - Part III,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:37.778477Z

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 a396723a-33a9-4f27-9f74-3f75fc923f03 · outbound

This paper cites an unresolved cited work.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:35:37.770580Z

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 df75ed84-7d10-4705-a5e9-34427d4a6832 · outbound

This paper cites Bachman and L.

A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation Bachman and L

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:35:37.762893Z

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.

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Outbound references

Pith citing papers