Paper Citation Record · LEDGER

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning

As of 23 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2507.04194.

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

pith.paper-citation-record.v1
2507.04194 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:02:32.803384Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

30 of 30 outbound references displayed

verified exact2
verified fuzzy21
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 94c9d912-9a36-41f1-868a-ea9d6eb30c69 · outbound

This paper cites Stability and hypothesis transfer learning.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Stability and hypothesis transfer learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.352633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0ef96446-ef43-4646-92ed-e4999712da30 · outbound

This paper cites Fast rates by transferring from auxiliary hypotheses.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Fast rates by transferring from auxiliary hypotheses

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.336895Z

Source-reported events for the cited work

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Observation 97626f7b-83d3-41c6-a691-d9233440c947 · outbound

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

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning On the hardness of domain adaptation and the utility of unlabeled target samples

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.322693Z

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Observation 63601497-0fcc-4e47-bff7-a0d81690d479 · outbound

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

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning On the value of target data in transfer learning

Reference 4

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Source-reported events for the cited work

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Observation 010198de-af50-48cd-a43a-05fa7a4d6a73 · outbound

This paper cites Adaptive Sample Aggregation In Transfer Learning.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Adaptive Sample Aggregation In Transfer Learning

Reference 5

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Source-reported events for the cited work

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Observation 22e9b1cb-854c-4105-ba91-027b8361e22f · outbound

This paper cites Stochastic gradient descent with only one projection.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Stochastic gradient descent with only one projection

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.299365Z

Source-reported events for the cited work

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Observation a18cfd69-d412-4c6d-8710-24eb84c1edf0 · outbound

This paper cites Learning bounds for domain adaptation.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Learning bounds for domain adaptation

Reference 7

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Source-reported events for the cited work

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Observation 44f7e6a2-fae2-4885-9516-c65a429bf70e · outbound

This paper cites Domain Adaptation: Learning Bounds and Algorithms.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Domain Adaptation: Learning Bounds and Algorithms

Reference 8

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Source-reported events for the cited work

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Observation 98faf0d8-69a9-423f-ace3-7f8a8e24c238 · outbound

This paper cites A theory of learning from different domains.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning A theory of learning from different domains

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.274856Z

Source-reported events for the cited work

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Observation ae5149f9-7f53-4e78-8b90-f07ba937a2f2 · outbound

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

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning New analysis and algorithm for learning with drifting distributions

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.260516Z

Source-reported events for the cited work

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Observation 7c1dd56e-6476-4fcb-8c35-bec28b608bef · outbound

This paper cites Density ratio estimation in machine learning.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Density ratio estimation in machine learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.245388Z

Source-reported events for the cited work

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Observation 77657119-0d3e-4aa3-832d-b66f0542c8e1 · outbound

This paper cites Density-ratio matching under the bregman diver- gence: a unified framework of density-ratio estimation.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Density-ratio matching under the bregman diver- gence: a unified framework of density-ratio estimation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.228455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 86539938-a246-4f2f-801c-a4170d573d4f · outbound

This paper cites Hypothesis transfer learning via transforma- tion functions.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Hypothesis transfer learning via transforma- tion functions

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.214138Z

Source-reported events for the cited work

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Observation c250c6c7-2992-47cb-a523-649b48cc66a0 · outbound

This paper cites Minimax lower bounds for transfer learning with linear and one-hidden layer neural networks.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Minimax lower bounds for transfer learning with linear and one-hidden layer neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.200519Z

Source-reported events for the cited work

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Observation 8ffdb3e5-8f37-4a4c-a323-9943271b9a00 · outbound

This paper cites A class of geometric structures in transfer learning: Minimax bounds and optimality.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning A class of geometric structures in transfer learning: Minimax bounds and optimality

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.185151Z

Source-reported events for the cited work

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Observation 49977762-ede9-4aa1-bc5a-0247c686bbf3 · outbound

This paper cites Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift

Reference 16

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Observation 4a6fc4a6-5b05-4e07-a36b-44774d305c2a · outbound

This paper cites Noisy recovery from random linear observations: Sharp minimax rates under elliptical constraints.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Noisy recovery from random linear observations: Sharp minimax rates under elliptical constraints

Reference 17

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Source-reported events for the cited work

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Observation 7744b9a8-e53d-4135-a8cd-320d8bfc2189 · outbound

This paper cites High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:02:32.903651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 437ce27e-7612-400a-84ec-cb2e258c6311 · outbound

This paper cites CVXPY: A Python-embedded modeling language for convex optimization.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning CVXPY: A Python-embedded modeling language for convex optimization

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.171447Z

Source-reported events for the cited work

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Observation ec5d6aa1-9be0-48ec-81c5-652c235d402a · outbound

This paper cites Uci machine learning repository, 2007.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Uci machine learning repository, 2007

Reference 20

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Observation beab7ac3-23af-4799-b0c0-19988c5b3b78 · outbound

This paper cites A distribution-dependent analysis of meta learning.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning A distribution-dependent analysis of meta learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.146221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 98c8c41e-8587-4415-a946-a1e924c2fa9a · outbound

This paper cites Learning multiple layers of features from tiny images.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Learning multiple layers of features from tiny images

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T20:02:31.976187Z

Source-reported events for the cited work

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Observation 0be67ffb-9b38-4a91-a71c-a4bbec8471c8 · outbound

This paper cites Understanding machine learning: From theory to algorithms.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Understanding machine learning: From theory to algorithms

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.121594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 12ece4e1-e7d8-41da-bc49-c7769b6ee2d7 · outbound

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

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Size-independent sample complexity of neural networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.107082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7bc6dd5f-9101-46b7-b0a9-add561a0e9af · outbound

This paper cites Foundations of machine learning.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Foundations of machine learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.092803Z

Source-reported events for the cited work

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Observation b69ee223-19cb-4d57-9c17-324a7860b5aa · outbound

This paper cites A century of portraits: A visual historical record of american high school yearbooks.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning A century of portraits: A visual historical record of american high school yearbooks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.076267Z

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Observation b4c5e80c-97ff-4409-99ee-ef8836418b2e · outbound

This paper cites Iot-23: A labeled dataset with malicious and benign iot network traffic.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Iot-23: A labeled dataset with malicious and benign iot network traffic

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.058526Z

Source-reported events for the cited work

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Observation 01cafb64-aa4c-4ec5-9f8a-f8764116ae7a · outbound

This paper cites Scikit-learn: Machine learning in python.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Scikit-learn: Machine learning in python

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.041015Z

Source-reported events for the cited work

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Observation 70d40f76-2ffb-4743-883d-e8b62af463bb · outbound

This paper cites Revisiting the last-iterate convergence of stochastic gradient methods.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning Revisiting the last-iterate convergence of stochastic gradient methods

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.023864Z

Source-reported events for the cited work

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Observation dc3ed08b-791e-449d-acaa-d90b9416b1b4 · outbound

This paper cites An optimal method for stochastic composite optimization.

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning An optimal method for stochastic composite optimization

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:02:33.005506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.