Paper Citation Record · LEDGER

Hybrid least squares for learning functions from highly noisy data

As of 8 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2507.02215.

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

pith.paper-citation-record.v1
2507.02215 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:47:25.684755Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

47 of 47 outbound references displayed

verified exact4
verified fuzzy29
unresolved14
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External citation measurements

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Outbound references

Observation 82ec56c1-2f14-4124-a9ef-8e2f6a1ad6d9 · outbound

This paper cites Adcock , Optimal sampling for least-squares approximation , Foundations of Computational Mathe- matics, (2025), pp.

Hybrid least squares for learning functions from highly noisy data Adcock , Optimal sampling for least-squares approximation , Foundations of Computational Mathe- matics, (2025), pp

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:31.192633Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 70bd36f8-d5c3-4995-9f23-49a8de9afbf7 · outbound

This paper cites Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations.

Hybrid least squares for learning functions from highly noisy data Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:47:26.452898Z

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Observation d6f60126-e55a-4107-ab30-1222fd42b021 · outbound

This paper cites Adcock, S.

Hybrid least squares for learning functions from highly noisy data Adcock, S

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:31.183104Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 23363193-01a1-42ec-a6f2-93f343169094 · outbound

This paper cites Adcock and J.

Hybrid least squares for learning functions from highly noisy data Adcock and J

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:31.174402Z

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Observation 0e0a82f9-1a0c-4c89-92c9-3375ee530e26 · outbound

This paper cites Alla and J.

Hybrid least squares for learning functions from highly noisy data Alla and J

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:31.166477Z

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Observation 69b018ed-a0da-4888-8150-f26fd2f3a6e4 · outbound

This paper cites A vron, M.

Hybrid least squares for learning functions from highly noisy data A vron, M

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:31.158117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cb798b69-8285-465c-ba82-431bec988bdf · outbound

This paper cites Bach, On the equivalence between kernel quadrature rules and random feature expansions , Journal of machine learning research, 18 (2017), pp.

Hybrid least squares for learning functions from highly noisy data Bach, On the equivalence between kernel quadrature rules and random feature expansions , Journal of machine learning research, 18 (2017), pp

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:31.150175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d5c4952c-0ac2-4578-b7d8-5f8e0d74364a · outbound

This paper cites Bendat and S.

Hybrid least squares for learning functions from highly noisy data Bendat and S

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:31.141146Z

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Observation 3ba2f3ca-dded-4648-acc3-ccaa3cbb3c0b · outbound

This paper cites Borwein and A.

Hybrid least squares for learning functions from highly noisy data Borwein and A

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:31.132498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8cfc129d-dcc1-4f16-9a72-706db1ff4ec7 · outbound

This paper cites Cohen, M.

Hybrid least squares for learning functions from highly noisy data Cohen, M

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:31.118875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d3cdbba5-81ec-494f-8984-e42e4221241c · outbound

This paper cites Cohen and G.

Hybrid least squares for learning functions from highly noisy data Cohen and G

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:31.106951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 85df40ef-69e1-4b34-a192-bdc4ea4d8185 · outbound

This paper cites an unresolved cited work.

Hybrid least squares for learning functions from highly noisy data Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:47:31.083326Z

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Observation 4f33676c-effd-4849-bf61-3260925ef173 · outbound

This paper cites an unresolved cited work.

Hybrid least squares for learning functions from highly noisy data Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:47:31.067230Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 65cb789b-345e-4743-b182-8a855564f281 · outbound

This paper cites Glasserman, Monte Carlo methods in financial engineering , vol.

Hybrid least squares for learning functions from highly noisy data Glasserman, Monte Carlo methods in financial engineering , vol

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:31.035613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0cf53c85-87a1-44bc-a597-a39ef1cb38cb · outbound

This paper cites an unresolved cited work.

Hybrid least squares for learning functions from highly noisy data Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:47:31.026579Z

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Observation b34e4edd-0e7d-4b4a-b99c-4fbb1af44927 · outbound

This paper cites an unresolved cited work.

Hybrid least squares for learning functions from highly noisy data Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:47:31.014735Z

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Observation 073dc1d8-7614-4d41-b3cc-242503ccba6d · outbound

This paper cites Haberstich, A.

Hybrid least squares for learning functions from highly noisy data Haberstich, A

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:30.999829Z

Source-reported events for the cited work

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Observation 655da6ab-a8dd-476f-b720-8c5b9d1ba812 · outbound

This paper cites Hadigol and A.

Hybrid least squares for learning functions from highly noisy data Hadigol and A

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:30.990627Z

Source-reported events for the cited work

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Observation 76928445-efd4-4be0-b8a1-82ae6e3a6b46 · outbound

This paper cites Herremans and B.

Hybrid least squares for learning functions from highly noisy data Herremans and B

Reference 19

Resolution: verified exact; raw_fallback, observed 2026-08-06T20:47:26.287221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 57243ccd-b28b-491d-9bff-58c964a64606 · outbound

This paper cites Differential Machine Learning.

Hybrid least squares for learning functions from highly noisy data Differential Machine Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:23.695680Z

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Observation 0c5a6394-27c0-4955-90c3-966b2c7ad0b1 · outbound

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Hybrid least squares for learning functions from highly noisy data Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:47:30.938359Z

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Observation d915a0bd-4a7d-45dc-8025-1d88da71ef79 · outbound

This paper cites Lewis, Finite dimensional subspaces of lp, Studia Mathematica, 63 (1978), pp.

Hybrid least squares for learning functions from highly noisy data Lewis, Finite dimensional subspaces of lp, Studia Mathematica, 63 (1978), pp

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:30.664154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 01f68443-e92d-4911-8599-4cae4f25bdac · outbound

This paper cites Li, Compressed Sensing and Matrix Completion with Constant Proportion of Corruptions, Constructive Approximation, 37 (2012), pp.

Hybrid least squares for learning functions from highly noisy data Li, Compressed Sensing and Matrix Completion with Constant Proportion of Corruptions, Constructive Approximation, 37 (2012), pp

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:30.353752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3cc2786c-1486-4867-9302-0ecbfa75ee64 · outbound

This paper cites an unresolved cited work.

Hybrid least squares for learning functions from highly noisy data Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:47:30.136331Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3fac349f-f256-496e-a154-4e462215b9e6 · outbound

This paper cites Fast algorithms for least square problems with Kronecker lower subsets.

Hybrid least squares for learning functions from highly noisy data Fast algorithms for least square problems with Kronecker lower subsets

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:47:26.029636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dea3feb0-1fcb-4dfc-ac67-565a601b33ea · outbound

This paper cites Martinsson and J.

Hybrid least squares for learning functions from highly noisy data Martinsson and J

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:29.855852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fa25bead-e3c8-4ecc-a0a1-d9c4b4fffb13 · outbound

This paper cites Matsuda and Y.

Hybrid least squares for learning functions from highly noisy data Matsuda and Y

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:29.657484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation adcdb13f-b3c3-4b73-af8e-c939f52118d9 · outbound

This paper cites Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software.

Hybrid least squares for learning functions from highly noisy data Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:24.210682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79f0f4ac-c5af-44d6-808c-9962fc0cb254 · outbound

This paper cites Narayan, J.

Hybrid least squares for learning functions from highly noisy data Narayan, J

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:29.551224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2f01b7ac-a473-482a-ad57-4ebc2f09aeab · outbound

This paper cites an unresolved cited work.

Hybrid least squares for learning functions from highly noisy data Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:47:29.389731Z

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Observation 0b8d4122-37d1-4421-a59d-6cabf997022a · outbound

This paper cites Nevai, G´ eza freud, orthogonal polynomials and christoffel functions.

Hybrid least squares for learning functions from highly noisy data Nevai, G´ eza freud, orthogonal polynomials and christoffel functions

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:29.192048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8706dfde-8b92-4716-98c2-d424bfa9f1be · outbound

This paper cites Niederreiter, Random number generation and quasi-Monte Carlo methods , SIAM, 1992.

Hybrid least squares for learning functions from highly noisy data Niederreiter, Random number generation and quasi-Monte Carlo methods , SIAM, 1992

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:29.036425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1c3124f3-b62c-4360-9f82-a5e1a6a8a617 · outbound

This paper cites Olivares, A.

Hybrid least squares for learning functions from highly noisy data Olivares, A

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:28.896170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9eaa7ec3-5ff0-4b2a-9e4f-7adbb3035055 · outbound

This paper cites Paszke, S.

Hybrid least squares for learning functions from highly noisy data Paszke, S

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:28.699907Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d985d4e2-2b34-4289-948f-bf945f091fcd · outbound

This paper cites Peherstorfer, Breaking the kolmogorov barrier with nonlinear model reduction, Notices of the Amer- ican Mathematical Society, 69 (2022), pp.

Hybrid least squares for learning functions from highly noisy data Peherstorfer, Breaking the kolmogorov barrier with nonlinear model reduction, Notices of the Amer- ican Mathematical Society, 69 (2022), pp

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:28.542410Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4742fe77-9bfb-4df7-8c9e-1d8e5fb9e603 · outbound

This paper cites Parametric Differential Machine Learning for Pricing and Calibration.

Hybrid least squares for learning functions from highly noisy data Parametric Differential Machine Learning for Pricing and Calibration

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:47:25.855530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 332d7632-ce1b-4e65-9060-f08f9791eff8 · outbound

This paper cites Pukelsheim, Optimal design of experiments , SIAM, 2006.

Hybrid least squares for learning functions from highly noisy data Pukelsheim, Optimal design of experiments , SIAM, 2006

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:28.396593Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e8150a20-82c3-4f88-96e8-92332919e34e · outbound

This paper cites Rahimi and B.

Hybrid least squares for learning functions from highly noisy data Rahimi and B

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:28.157452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 32091aa6-69e3-49a1-ad92-2eec13072d35 · outbound

This paper cites Reiss and M.

Hybrid least squares for learning functions from highly noisy data Reiss and M

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:28.001677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation be642432-e8dc-47a4-9200-2e1c166c7188 · outbound

This paper cites Shin and D.

Hybrid least squares for learning functions from highly noisy data Shin and D

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:27.832535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 93fff7fa-3282-462b-a25a-924b0a3c5504 · outbound

This paper cites an unresolved cited work.

Hybrid least squares for learning functions from highly noisy data Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:47:27.550532Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 57b4b66f-ebbb-4912-b6e3-3081b88a1e4c · outbound

This paper cites V apnik, Principles of risk minimization for learning theory , Advances in Neural Information Process- ing Systems, 4 (1991).

Hybrid least squares for learning functions from highly noisy data V apnik, Principles of risk minimization for learning theory , Advances in Neural Information Process- ing Systems, 4 (1991)

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:27.407351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 520b23fb-2db1-41f5-b3ec-e1ea1c006aef · outbound

This paper cites an unresolved cited work.

Hybrid least squares for learning functions from highly noisy data Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:47:27.246217Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ce95f50b-918f-45d7-b157-5338d2ce93f8 · outbound

This paper cites an unresolved cited work.

Hybrid least squares for learning functions from highly noisy data Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:47:27.015246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 609e9df9-9df2-4165-a0ae-e90a6fa0535b · outbound

This paper cites Xiu, Numerical methods for stochastic computations: a spectral method approach, Princeton University Press, 2010.

Hybrid least squares for learning functions from highly noisy data Xiu, Numerical methods for stochastic computations: a spectral method approach, Princeton University Press, 2010

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T20:47:25.525629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6382842-efc3-484f-8410-e1ffd227f2c7 · outbound

This paper cites Xu and A.

Hybrid least squares for learning functions from highly noisy data Xu and A

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:47:26.838269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c9f2f5f9-4f11-4aaa-b631-9b31a1fe3aba · outbound

This paper cites an unresolved cited work.

Hybrid least squares for learning functions from highly noisy data Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:47:26.686761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.