Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T20:47:25.684755Z
Paper Citation Record · LEDGER
Hybrid least squares for learning functions from highly noisy data
As of 18 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0c5a6394-27c0-4955-90c3-966b2c7ad0b1 · outbound
This paper cites an unresolved cited work.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.