When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts | Paper Citation Record

Observation 20fefa69-c026-44cb-9b24-ae029da880cb · outbound

This paper cites Achieving robustness across season, location and cultivar for a nirs model for intact mango fruit dry matter content.Postharvest Biology and Technology, 168:111202, 2020.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Achieving robustness across season, location and cultivar for a nirs model for intact mango fruit dry matter content.Postharvest Biology and Technology, 168:111202, 2020

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This paper cites Invariant Risk Minimization.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Invariant Risk Minimization

Reference 2

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Observation 5992ebfd-92c7-401a-ad91-f9555a39af39 · outbound

This paper cites MIT press, 2024.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts MIT press, 2024

Reference 3

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Observation ac4818a0-8539-4674-9e49-e807067fada4 · outbound

This paper cites Un- supervised domain adaptation by domain invariant projection.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Un- supervised domain adaptation by domain invariant projection

Reference 4

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Observation 8de1e97f-39e8-4c6a-829d-c3480fc3497d · outbound

This paper cites Predicting with proxies: Transfer learning in high dimension.Management Science, 67(5):2964–2984, 2021.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Predicting with proxies: Transfer learning in high dimension.Management Science, 67(5):2964–2984, 2021

Reference 5

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Observation 339b2db4-569e-43b4-8fe2-1b8e8b5d74be · outbound

This paper cites A theory of learning from different domains.Machine learning, 79 (1-2):151–175, 2010.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts A theory of learning from different domains.Machine learning, 79 (1-2):151–175, 2010

Reference 6

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Observation 19aaac47-51db-4b0f-b733-0d2d8bd60110 · outbound

This paper cites Springer Science & Business Media, 2013.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Springer Science & Business Media, 2013

Reference 7

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Observation 0747a344-9d93-4034-857f-41ab40fb8ad5 · outbound

This paper cites Simultaneous analysis of lasso and dantzig selector.The Annals of Statistics, 37(4):1705, 2009.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Simultaneous analysis of lasso and dantzig selector.The Annals of Statistics, 37(4):1705, 2009

Reference 8

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Observation 63fafed4-3356-4489-bd2f-1db1f78571b2 · outbound

This paper cites Combining labeled and unlabeled data with co-training.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Combining labeled and unlabeled data with co-training

Reference 9

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Observation 33f7d6a3-9646-42ea-87d1-f6eb30780602 · outbound

This paper cites Invariance, causality and robustness.Statistical Science, 35(3):404–426, 2020.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Invariance, causality and robustness.Statistical Science, 35(3):404–426, 2020

Reference 10

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Observation 6675a5c9-ae1c-4427-aa4d-f6364eca7748 · outbound

This paper cites Causality matters in medical imaging.Nature Communications, 11(1):3673, 2020.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Causality matters in medical imaging.Nature Communications, 11(1):3673, 2020

Reference 11

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Observation 71dc9ac8-8f7a-4763-9c6b-96e35dfc928a · outbound

This paper cites An empirical study of training self-supervised vision transformers.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts An empirical study of training self-supervised vision transformers

Reference 12

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Observation d89b9aec-23b4-475f-8e3a-0ebfddb07cef · outbound

This paper cites Domain adaptation under structural causal models.Journal of Machine Learning Research, 22(261):1–80, 2021.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Domain adaptation under structural causal models.Journal of Machine Learning Research, 22(261):1–80, 2021

Reference 13

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Observation 367809b7-ed88-46c9-a375-d889573c932e · outbound

This paper cites Spectral methods for data science: A statistical perspective.Foundations and Trends®in Machine Learning, 14(5):566–806, 2021.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Spectral methods for data science: A statistical perspective.Foundations and Trends®in Machine Learning, 14(5):566–806, 2021

Reference 14

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Observation 3725038d-551f-4fac-9432-cfb6ceb5cb5a · outbound

This paper cites Optimal transport for domain adaptation.IEEE transactions on pattern analysis and machine intelligence, 39(9): 1853–1865, 2016.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Optimal transport for domain adaptation.IEEE transactions on pattern analysis and machine intelligence, 39(9): 1853–1865, 2016

Reference 15

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Observation 677fdf0d-743a-4766-a3b1-b99fb09ac39a · outbound

This paper cites The Bayesian Approach To Inverse Problems.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts The Bayesian Approach To Inverse Problems

Reference 16

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Observation 5c724051-f717-4b33-9afa-f9361e32e349 · outbound

This paper cites Semi-supervised domain adaptation with instance constraints.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Semi-supervised domain adaptation with instance constraints

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.639805Z

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Observation 0a48ba84-7bbf-437d-9478-26ec962044b9 · outbound

This paper cites Statistics of robust optimization: A generalized empirical likelihood approach.Mathematics of Operations Research, 46(3):946–969, 2021.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Statistics of robust optimization: A generalized empirical likelihood approach.Mathematics of Operations Research, 46(3):946–969, 2021

Reference 18

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Observation c29aff48-3d68-45ca-a910-4387d3cfb3d3 · outbound

This paper cites Causal chambers as a real-world physical testbed for ai methodology.Nature Machine Intelligence, 7(1):107–118, 2025.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Causal chambers as a real-world physical testbed for ai methodology.Nature Machine Intelligence, 7(1):107–118, 2025

Reference 19

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Observation e0f1614d-2782-46b6-8d84-c3d972766079 · outbound

This paper cites Domain-adversarial training of neural networks.The Journal of Machine Learning Research, 17(1):2096–2030, 2016.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Domain-adversarial training of neural networks.The Journal of Machine Learning Research, 17(1):2096–2030, 2016

Reference 20

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Observation d59659ee-3b15-43af-978d-1664e13bff2a · outbound

This paper cites Shortcut learning in deep neural networks.Nature Machine Intelligence, 2(11):665–673, 2020.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Shortcut learning in deep neural networks.Nature Machine Intelligence, 2(11):665–673, 2020

Reference 21

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This paper cites Domain adaptation with conditional transferable components.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Domain adaptation with conditional transferable components

Reference 22

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Observation 1c849623-4eca-42fe-809e-3823188f30b7 · outbound

This paper cites Improving neural network training in low dimensional random bases.Advances in Neural Information Processing Systems, 33: 12140–12150, 2020.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Improving neural network training in low dimensional random bases.Advances in Neural Information Processing Systems, 33: 12140–12150, 2020

Reference 23

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Observation 691a4b61-f37c-46e3-a467-14e1ad57ae6a · outbound

This paper cites A kernel two-sample test.The Journal of Machine Learning Research, 13(1):723–773, 2012.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts A kernel two-sample test.The Journal of Machine Learning Research, 13(1):723–773, 2012

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.536756Z

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Observation b53d242f-471c-4868-ab9e-877c59bff05f · outbound

This paper cites Domain adaptation for medical image analysis: A survey.IEEE Transactions on Biomedical Engineering, 2022.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Domain adaptation for medical image analysis: A survey.IEEE Transactions on Biomedical Engineering, 2022

Reference 25

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Observation e625611c-78ae-4972-9d7f-55b3695344fe · outbound

This paper cites In search of lost domain generalization.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts In search of lost domain generalization

Reference 26

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This paper cites Adap- tive wavelet distillation from neural networks through interpretations.Advances in Neural Information Processing Systems, 34:20669–20682, 2021.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Adap- tive wavelet distillation from neural networks through interpretations.Advances in Neural Information Processing Systems, 34:20669–20682, 2021

Reference 27

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This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 28

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Observation f3e81010-7157-42e4-aa34-516e9038e839 · outbound

This paper cites Conditional variance penalties and domain shift robustness.Machine Learning, 110(2):303–348, 2021.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Conditional variance penalties and domain shift robustness.Machine Learning, 110(2):303–348, 2021

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.468032Z

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Observation 2d90014c-8575-4389-8282-ea18c77c2f67 · outbound

This paper cites Benchmarking neural network robustness to common corruptions and perturbations.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Benchmarking neural network robustness to common corruptions and perturbations

Reference 30

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Observation 9138f148-e4fe-42f4-8b06-4327d7fad10e · outbound

This paper cites Random design analysis of ridge regression.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Random design analysis of ridge regression

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.442221Z

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This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1 (2):3, 2022.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Lora: Low-rank adaptation of large language models.ICLR, 1 (2):3, 2022

Reference 32

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Observation dbe84604-c37e-4bfc-9f5b-22fa0c14661e · outbound

This paper cites Bidirectional adversarial training for semi-supervised domain adaptation.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Bidirectional adversarial training for semi-supervised domain adaptation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.416997Z

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Observation a9d47155-5796-46f5-aa7f-97b754f59a2d · outbound

This paper cites Support and invertibility in domain-invariant representations.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Support and invertibility in domain-invariant representations

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.401038Z

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Observation 4f48fab0-49f7-4a6b-9c30-c3c0cfcb06ca · outbound

This paper cites Do better imagenet models transfer better? InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2661–2671, 2019.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Do better imagenet models transfer better? InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2661–2671, 2019

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.385971Z

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Observation a4c4f424-6970-4c98-9b45-a563b8e4af62 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012

Reference 36

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This paper cites Out-of-distribution generalization via risk extrapolation (rex).

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Out-of-distribution generalization via risk extrapolation (rex)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.357141Z

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Observation 3efab4f3-1ca1-47bd-a33f-cd99bd2d47aa · outbound

This paper cites How to fine-tune vision models with sgd.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts How to fine-tune vision models with sgd

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Observation ef79aeb8-90fc-4687-9998-87e1b9d0da10 · outbound

This paper cites Distributional Robustness and Transfer Learning Through Empirical Bayes.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Distributional Robustness and Transfer Learning Through Empirical Bayes

Reference 39

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Observation 4b986eda-8a6a-4f66-ac63-ffbc83413cec · outbound

This paper cites Measuring the intrinsic dimension of objective landscapes.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Measuring the intrinsic dimension of objective landscapes

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.324055Z

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Observation 8f4a323b-02e9-4f5b-95b6-c402cce3cea8 · outbound

This paper cites an unresolved cited work.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:09:19.308111Z

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Observation da2fd152-90ce-4e09-91f2-36cbc9e441b8 · outbound

This paper cites Deep domain generalization via conditional invariant adversarial networks.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Deep domain generalization via conditional invariant adversarial networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.291053Z

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Observation 81dbb845-55bd-4fe0-a426-e58545170550 · outbound

This paper cites A simple tool for bounding the deviation of random matrices on geometric sets.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts A simple tool for bounding the deviation of random matrices on geometric sets

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.274315Z

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Observation 4890b23e-d719-4d38-9a6d-82c8f003e5ad · outbound

This paper cites Semi-supervised domain adaptation for automatic quality control of flair mris in a clinical data warehouse.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Semi-supervised domain adaptation for automatic quality control of flair mris in a clinical data warehouse

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.258613Z

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Observation d867c575-fb15-4a80-ac51-989fd5bdf55b · outbound

This paper cites Concentration inequalities under sub-gaussian and sub-exponential conditions.Advances in Neural Information Processing Systems, 34:7588– 7597, 2021.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Concentration inequalities under sub-gaussian and sub-exponential conditions.Advances in Neural Information Processing Systems, 34:7588– 7597, 2021

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.240341Z

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Observation 4dbc31de-516e-4c0a-80a6-f8da94d5b375 · outbound

This paper cites Marginal likelihood for distance matrices.Statistica Sinica, pages 631–649, 2009.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Marginal likelihood for distance matrices.Statistica Sinica, pages 631–649, 2009

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.222292Z

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Observation f8f5d7a5-fb71-4592-9721-6d16275c75d0 · outbound

This paper cites A brief note on application of domain-invariant pls for adapting near-infrared spectroscopy calibrations between different physical forms of samples.Talanta, 232:122461, 2021.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts A brief note on application of domain-invariant pls for adapting near-infrared spectroscopy calibrations between different physical forms of samples.Talanta, 232:122461, 2021

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.204296Z

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Observation 9f65c20c-0997-46b2-a855-55405b26ecbd · outbound

This paper cites Exact minimax risk for linear least squares, and the lower tail of sample covariance matrices.The Annals of Statistics, 50(4):2157–2178, 2022.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Exact minimax risk for linear least squares, and the lower tail of sample covariance matrices.The Annals of Statistics, 50(4):2157–2178, 2022

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.188083Z

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 c4589247-8dc5-476d-b8d0-6519faddcec4 · outbound

This paper cites Domain-invariant partial-least-squares regression.Analytical chemistry, 90(11):6693–6701, 2018.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Domain-invariant partial-least-squares regression.Analytical chemistry, 90(11):6693–6701, 2018

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.171356Z

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 52c2b014-0bc5-4f1c-84dc-e03084aee66c · outbound

This paper cites Random perturbation of low rank matrices: Improving classical bounds.Linear Algebra and its Applications, 540:26–59, 2018.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Random perturbation of low rank matrices: Improving classical bounds.Linear Algebra and its Applications, 540:26–59, 2018

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.154942Z

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 e20d7cf8-33ca-487a-b540-64b1450d0dc0 · outbound

This paper cites A quantitative formulation of sylvester’s law of inertia.Proceedings of the National Academy of Sciences, 45(5):740–744, 1959.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts A quantitative formulation of sylvester’s law of inertia.Proceedings of the National Academy of Sciences, 45(5):740–744, 1959

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.138692Z

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 6b7bc51a-1094-430f-a59c-178173975959 · outbound

This paper cites Cambridge university press, 2009.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Cambridge university press, 2009

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T16:09:11.324129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7fc5c0c-9563-476e-8b6d-1b7a91315f71 · outbound

This paper cites Moment matching for multi-source domain adaptation.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Moment matching for multi-source domain adaptation

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T16:09:11.439040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86f9e5e1-4a63-4ca2-9c1e-2f1a3d9943c9 · outbound

This paper cites an unresolved cited work.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:09:19.098195Z

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 7245532f-adc1-4740-b482-7d5e21a9e06b · outbound

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

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts A survey on domain adaptation theory: learning bounds and theoretical guarantees

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Resolution: unresolved; no resolver link, observed 2026-08-06T16:09:12.865180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dc51675-4621-4a49-bf92-4cfee8177dd3 · outbound

This paper cites An improved cosmological parameter inference scheme motivated by deep learning.Nature Astronomy, 3(1):93–98, 2019.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts An improved cosmological parameter inference scheme motivated by deep learning.Nature Astronomy, 3(1):93–98, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.081022Z

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 3f060870-8873-4844-97eb-35e2fb9b20a8 · outbound

This paper cites An empirical bayes approach to statistics.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts An empirical bayes approach to statistics

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.062552Z

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 64de9a61-e275-4b77-a788-c96f49b0782f · outbound

This paper cites Cloning instru- ments, model maintenance and calibration transfer.TrAC Trends in Analytical Chemistry, 191:118319, 2025.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Cloning instru- ments, model maintenance and calibration transfer.TrAC Trends in Analytical Chemistry, 191:118319, 2025

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.045076Z

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 ec2ea1f9-8d16-49a9-a9d8-3e92964e97fe · outbound

This paper cites The Risks of Invariant Risk Minimization.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts The Risks of Invariant Risk Minimization

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Resolution: unresolved; no resolver link, observed 2026-08-06T16:09:13.290759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61edc108-c69b-4526-bb0d-eab94b09d13c · outbound

This paper cites Causal dantzig.The Annals of Statistics, 47(3):1688–1722, 2019.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Causal dantzig.The Annals of Statistics, 47(3):1688–1722, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.026173Z

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 a145bf45-57ff-4c10-baaf-8dd448ee29de · outbound

This paper cites Anchor regression: Heterogeneous data meet causality.Journal of the Royal Statistical Society Series B: Statistical Methodology, 83(2):215–246, 2021.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Anchor regression: Heterogeneous data meet causality.Journal of the Royal Statistical Society Series B: Statistical Methodology, 83(2):215–246, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:19.008026Z

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 ee59ab5d-b0e5-4b0a-9f2f-72ec30f20988 · outbound

This paper cites Hanson-wright inequality and sub-gaussian concentra- tion.Electronic Communications in Probability, 18:1–9, 2013.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Hanson-wright inequality and sub-gaussian concentra- tion.Electronic Communications in Probability, 18:1–9, 2013

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:18.984489Z

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 d15de747-050c-4709-84b3-a77ae576affb · outbound

This paper cites Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization

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Resolution: unresolved; no resolver link, observed 2026-08-06T16:09:13.665071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1eb32a07-692b-48b8-83f4-b533d8456d24 · outbound

This paper cites Semi- supervised domain adaptation via minimax entropy.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Semi- supervised domain adaptation via minimax entropy

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:18.955520Z

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 75c62a20-350d-4470-a98e-8c6bb3fbe23d · outbound

This paper cites On causal and anticausal learning.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts On causal and anticausal learning

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:18.937257Z

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 ecd6f28d-2ba8-4960-93b0-4c38cd6bbe1b · outbound

This paper cites Causality-oriented robustness: Exploiting general noise interventions.Journal of the American Statistical Association, 121(553):704–715, 2026.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Causality-oriented robustness: Exploiting general noise interventions.Journal of the American Statistical Association, 121(553):704–715, 2026

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:18.920107Z

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 bb709484-080a-4776-9ef5-20d699e5cdfd · outbound

This paper cites Deep coral: Correlation alignment for deep domain adaptation.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Deep coral: Correlation alignment for deep domain adaptation

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Resolution: unresolved; no resolver link, observed 2026-08-06T16:09:14.054647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3409b921-5b05-4b2e-ae9f-315c225994df · outbound

This paper cites an unresolved cited work.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:09:18.890380Z

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 434618e7-d8c7-4dab-859b-27763eb3963a · outbound

This paper cites Achieving robustness to temperature change of a nirs-plsr model for intact mango fruit dry matter content.Postharvest Biology and Tech- nology, 162:111117, 2020.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Achieving robustness to temperature change of a nirs-plsr model for intact mango fruit dry matter content.Postharvest Biology and Tech- nology, 162:111117, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:18.860153Z

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 6604ee9a-f541-4588-809c-4ed03f805f1a · outbound

This paper cites Springer Series in Statistics, 2009.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Springer Series in Statistics, 2009

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:18.778942Z

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 bc65fdbd-c233-46d0-817c-e6476063a585 · outbound

This paper cites Weak convergence and empirical processes with ap- plications to statistics.Journal of the Royal Statistical Society-Series A Statistics in Society, 160(3):596–608, 1997.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Weak convergence and empirical processes with ap- plications to statistics.Journal of the Royal Statistical Society-Series A Statistics in Society, 160(3):596–608, 1997

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:18.673288Z

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 820493b0-c580-4978-85a0-9335ead0bd15 · outbound

This paper cites A survey on semi-supervised learning.Machine learning, 109(2):373–440, 2020.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts A survey on semi-supervised learning.Machine learning, 109(2):373–440, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:18.520161Z

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 9cb89077-3c2a-4103-aee1-f7d13699f411 · outbound

This paper cites Cambridge university press, 2018.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Cambridge university press, 2018

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T16:09:14.690610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 627676b8-e84e-4e72-bdb9-f1d8034a2e0d · outbound

This paper cites Cambridge University Press, 2019.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Cambridge University Press, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:18.357108Z

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 9d37f7d9-a7e1-426f-a8be-8b7ae39be3ed · outbound

This paper cites A survey of unsupervised deep domain adaptation.ACM Transactions on Intelligent Systems and Technology (TIST), 11(5):1–46, 2020.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts A survey of unsupervised deep domain adaptation.ACM Transactions on Intelligent Systems and Technology (TIST), 11(5):1–46, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:18.201953Z

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 cc4657c3-65fe-47d5-a5d2-890ef95f4dee · outbound

This paper cites Prominent roles of conditionally invariant components in domain adaptation: Theory and algorithms.Journal of Machine Learning Research, 26(110):1–92, 2025.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Prominent roles of conditionally invariant components in domain adaptation: Theory and algorithms.Journal of Machine Learning Research, 26(110):1–92, 2025

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:18.151359Z

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 1fc437d5-b5d2-4906-bee9-ed506fcf5894 · outbound

This paper cites Distributionally Robust Transfer Learning.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Distributionally Robust Transfer Learning

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Resolution: unresolved; no resolver link, observed 2026-08-06T16:09:15.238284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd3c5074-3380-4979-b4df-959078609988 · outbound

This paper cites Multi-level Consistency Learning for Semi-supervised Domain Adaptation.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Multi-level Consistency Learning for Semi-supervised Domain Adaptation

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Resolution: verified exact; local_arxiv, observed 2026-08-06T16:09:16.325571Z

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 1d07061f-6df6-4515-8d3f-21c1d1b3282c · outbound

This paper cites Deep co-training with task decomposition for semi-supervised domain adaptation.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Deep co-training with task decomposition for semi-supervised domain adaptation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:18.012148Z

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 9aa36189-beb6-42ad-bfaa-70397fbd0826 · outbound

This paper cites Im- proving domain generalization with domain relations.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Im- proving domain generalization with domain relations

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:17.874158Z

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 51026325-f620-402d-8149-0400523a214e · outbound

This paper cites A useful variant of the davis–kahan theorem for statisticians.Biometrika, 102(2):315–323, 2015.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts A useful variant of the davis–kahan theorem for statisticians.Biometrika, 102(2):315–323, 2015

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:17.685904Z

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 c43d6110-5162-40f7-9df4-154c48a3c994 · outbound

This paper cites Semi-supervised domain adaptation with source label adapta- tion.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Semi-supervised domain adaptation with source label adapta- tion

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:17.618021Z

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 9b1e93a4-cca7-4d6d-a0f4-dd5d31083ca8 · outbound

This paper cites On learning invariant representations for domain adaptation.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts On learning invariant representations for domain adaptation

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Resolution: unresolved; no resolver link, observed 2026-08-06T16:09:15.771737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9b747ec-3e09-4a3b-b640-df3f1ca66ca1 · outbound

This paper cites Pls subspace- based calibration transfer for near-infrared spectroscopy quantitative analysis.Molecules, 24 (7):1289, 2019.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Pls subspace- based calibration transfer for near-infrared spectroscopy quantitative analysis.Molecules, 24 (7):1289, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:17.379929Z

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 adeae7a9-c5da-4c4d-9df9-7eff564381ec · outbound

This paper cites Calibration transfer based on affine invariance for nir without transfer standards.Molecules, 24(9):1802, 2019.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Calibration transfer based on affine invariance for nir without transfer standards.Molecules, 24(9):1802, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:17.138311Z

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 7d5a1797-5e40-42b2-8498-caefb45b0107 · outbound

This paper cites Domain generalization: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(4):4396–4415, 2022.

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts Domain generalization: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(4):4396–4415, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:09:16.931317Z

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.

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