Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2410.23440.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T19:58:40.761327Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T00:56:24.104633Z
Reference resolution
0 of 0 outbound references displayed
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- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 5db391ec-6c89-46ed-9a3b-bb78d9d1a9f9 · inbound
A short tour of operator learning theory: Convergence rates, statistical limits, and open questions cites this paper.
A short tour of operator learning theory: Convergence rates, statistical limits, and open questions The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T19:58:40.761327Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8f219eaf-43d5-4fea-a1e6-0dd11a5f387e · inbound
Upper Generalization Bounds for Neural Oscillators cites this paper.
Upper Generalization Bounds for Neural Oscillators The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T13:25:50.442476Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f9b0a6b9-0ee7-46c3-b8b0-ff1eb677b188 · inbound
Universal, sample-optimal algorithms for recovery of anisotropic functions from i.i.d. samples cites this paper.
Universal, sample-optimal algorithms for recovery of anisotropic functions from i.i.d. samples The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T07:55:59.542635Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 13b275d6-9b28-44db-ba1c-e4ed63f7873a · inbound
Cellular Sheaf Neural Operators for Structure-Preserving Surrogate Modeling of Constrained PDEs cites this paper.
Cellular Sheaf Neural Operators for Structure-Preserving Surrogate Modeling of Constrained PDEs The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
Reference 66
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T20:46:13.285943Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b0f873f6-da9c-49ba-ac97-84b20049cc9b · inbound
Efficient Approximation for Encoder--Decoder Neural Operators via Variation Spaces cites this paper.
Efficient Approximation for Encoder--Decoder Neural Operators via Variation Spaces The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
Reference 1
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T21:46:15.426064Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 0a45902b-087d-425b-8203-a388ea175823 · inbound
Transpose-free linear algebra cites this paper.
Transpose-free linear algebra The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T21:46:15.465707Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 54205eca-6279-489c-a0e2-f4cdb8537a47 · inbound
From Spectral Methods to Sample Complexity Bounds for Fourier Neural Operators cites this paper.
From Spectral Methods to Sample Complexity Bounds for Fourier Neural Operators The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T00:56:24.106196Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.