Paper Citation Record · LEDGER

Random feature approximation for general spectral methods

As of 12 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2506.16283.

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

pith.paper-citation-record.v1
2506.16283 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:54:53.283304Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e3e6dcdb-a602-4f06-96e3-f07b5e713670 · outbound

This paper cites Operator H\"older--Zygmund functions.

Random feature approximation for general spectral methods Operator H\"older--Zygmund functions

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:54:53.991549Z

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Observation 4b09e387-f0ed-4963-9191-b79d5d78d918 · outbound

This paper cites Katyusha: The first direct acceleration of stochastic gradient methods.

Random feature approximation for general spectral methods Katyusha: The first direct acceleration of stochastic gradient methods

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.623777Z

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Observation 1d62711a-0fc9-41d5-b210-76345a4e67f3 · outbound

This paper cites Optimal rates for regularization of statistical inverse learning problems.

Random feature approximation for general spectral methods Optimal rates for regularization of statistical inverse learning problems

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.608938Z

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Observation 6b0984db-e614-4070-b680-210f364dc1b3 · outbound

This paper cites Caponnetto and Ernesto De Vito.

Random feature approximation for general spectral methods Caponnetto and Ernesto De Vito

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.593315Z

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Observation c2201b39-3953-4040-ab16-6b149be80ca1 · outbound

This paper cites Carmeli, E.

Random feature approximation for general spectral methods Carmeli, E

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.575823Z

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Observation e56761bc-8c6f-49a2-a8d1-80c7340330ab · outbound

This paper cites Reproducing kernel hilbert spaces and mercer theorem, 2005.

Random feature approximation for general spectral methods Reproducing kernel hilbert spaces and mercer theorem, 2005

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.559430Z

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Observation 7e370617-b24e-46ef-9f50-66e3f33d99e7 · outbound

This paper cites Learning with sgd and random features, 2019.

Random feature approximation for general spectral methods Learning with sgd and random features, 2019

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.541805Z

Source-reported events for the cited work

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Observation 46d70a40-3447-467e-be31-3e1887ff072f · outbound

This paper cites On the impact of kernel approximation on learning accuracy.

Random feature approximation for general spectral methods On the impact of kernel approximation on learning accuracy

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.525303Z

Source-reported events for the cited work

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Observation ba17f7da-1489-442d-9a36-6a5f19e6c405 · outbound

This paper cites Every model learned by gradient descent is approximately a kernel machine, 2020.

Random feature approximation for general spectral methods Every model learned by gradient descent is approximately a kernel machine, 2020

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.507668Z

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Observation 72026914-43a9-472f-ba69-4f0f496bad01 · outbound

This paper cites On the nystr \"o m method for approximating a gram matrix for improved kernel-based learning.

Random feature approximation for general spectral methods On the nystr \"o m method for approximating a gram matrix for improved kernel-based learning

Reference 10

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Source-reported events for the cited work

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Observation 19c64a3f-9dfb-41c2-a562-5d3010634912 · outbound

This paper cites Regularization of inverse problems, volume 375.

Random feature approximation for general spectral methods Regularization of inverse problems, volume 375

Reference 11

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Source-reported events for the cited work

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Observation 0735f457-f916-4885-9c0e-737fb20c8ff8 · outbound

This paper cites Stochastic heavy ball.

Random feature approximation for general spectral methods Stochastic heavy ball

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T23:54:50.677400Z

Source-reported events for the cited work

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Observation 25dbcacb-3237-4cdf-893b-1114b53792d9 · outbound

This paper cites Lo Gerfo, L.

Random feature approximation for general spectral methods Lo Gerfo, L

Reference 13

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Source-reported events for the cited work

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Observation b38ed649-3296-49db-960a-286cc76baaf0 · outbound

This paper cites Global convergence of the heavy-ball method for convex optimization.

Random feature approximation for general spectral methods Global convergence of the heavy-ball method for convex optimization

Reference 14

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Observation 532bed9a-30cb-45ed-a72e-e0d5469a51e2 · outbound

This paper cites Nelsen, and Margaret Trautner.

Random feature approximation for general spectral methods Nelsen, and Margaret Trautner

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.467454Z

Source-reported events for the cited work

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Observation 7f1e12d0-3f8e-4609-9ba1-3fa0a041f349 · outbound

This paper cites Neural tangent kernel: Convergence and generalization in neural networks.

Random feature approximation for general spectral methods Neural tangent kernel: Convergence and generalization in neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.451410Z

Source-reported events for the cited work

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Observation d9b158bf-83d6-499f-8859-8ac9146c4c5b · outbound

This paper cites Kovachki, Zong-Yi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M.

Random feature approximation for general spectral methods Kovachki, Zong-Yi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.435962Z

Source-reported events for the cited work

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Observation 9a8e1b58-7fc4-475c-a3a9-32abf2aff9b0 · outbound

This paper cites Kovachki, Samuel Lanthaler, and Andrew M.

Random feature approximation for general spectral methods Kovachki, Samuel Lanthaler, and Andrew M

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.420686Z

Source-reported events for the cited work

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Observation 442201f3-326e-486d-944c-cdac8d635843 · outbound

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Random feature approximation for general spectral methods Unresolved cited work

Reference 19

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Observation b6ee967e-1beb-4714-970a-fdf65f421c42 · outbound

This paper cites Kwok, and Bao-Liang Lu.

Random feature approximation for general spectral methods Kwok, and Bao-Liang Lu

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.391299Z

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Random feature approximation for general spectral methods Unresolved cited work

Reference 21

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Observation 2b120602-b51b-4dac-ae0e-c24adfddbf12 · outbound

This paper cites Towards a unified analysis of random fourier features, 2021 b.

Random feature approximation for general spectral methods Towards a unified analysis of random fourier features, 2021 b

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.360947Z

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Observation f02e02ff-e41b-44b7-bc69-6d0e923f829e · outbound

This paper cites Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms.

Random feature approximation for general spectral methods Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:54:53.647209Z

Source-reported events for the cited work

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Observation 9e90357e-9c5a-4815-85d6-f6acf1ea11f8 · outbound

This paper cites Optimal rates for spectral algorithms with least-squares regression over hilbert spaces.

Random feature approximation for general spectral methods Optimal rates for spectral algorithms with least-squares regression over hilbert spaces

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.344013Z

Source-reported events for the cited work

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Observation 7593c6c4-9967-41e6-ae6d-af58ef9cacea · outbound

This paper cites Woodruff.

Random feature approximation for general spectral methods Woodruff

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.327568Z

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Observation c1798274-3487-4766-808e-ba9c432c0efe · outbound

This paper cites How many neurons do we need? a refined analysis for shallow networks trained with gradient descent, 2023.

Random feature approximation for general spectral methods How many neurons do we need? a refined analysis for shallow networks trained with gradient descent, 2023

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.311335Z

Source-reported events for the cited work

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Observation b523dbd8-eafc-4d2c-9a4c-ea3805806dab · outbound

This paper cites Optimal Convergence Rates for Neural Operators.

Random feature approximation for general spectral methods Optimal Convergence Rates for Neural Operators

Reference 27

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Source-reported events for the cited work

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Observation 47ff3d25-7564-4dd8-b1cb-dacd437d53dd · outbound

This paper cites Optimal rates for averaged stochastic gradient descent under neural tangent kernel regime.

Random feature approximation for general spectral methods Optimal rates for averaged stochastic gradient descent under neural tangent kernel regime

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.295127Z

Source-reported events for the cited work

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Observation 7a8a98f1-7c87-41c7-9827-3f3312429b1b · outbound

This paper cites Towards moderate overparameterization: global convergence guarantees for training shallow neural networks, 2019.

Random feature approximation for general spectral methods Towards moderate overparameterization: global convergence guarantees for training shallow neural networks, 2019

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.277517Z

Source-reported events for the cited work

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Observation 69860332-dfc3-4ff6-958f-8541a0a79f50 · outbound

This paper cites Implicit regularization of accelerated methods in hilbert spaces.

Random feature approximation for general spectral methods Implicit regularization of accelerated methods in hilbert spaces

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.261847Z

Source-reported events for the cited work

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Observation e4b8d719-7251-4d4f-a560-2e8287918328 · outbound

This paper cites Statistical optimality of stochastic gradient descent on hard learning problems through multiple passes, 2018.

Random feature approximation for general spectral methods Statistical optimality of stochastic gradient descent on hard learning problems through multiple passes, 2018

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:57.099054Z

Source-reported events for the cited work

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Observation 75fe94a7-7aa7-4f34-b202-bebadea83258 · outbound

This paper cites Random features for large-scale kernel machines.

Random feature approximation for general spectral methods Random features for large-scale kernel machines

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:56.832668Z

Source-reported events for the cited work

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Observation 6fabb395-e84d-466d-9f4d-10dcf8e333df · outbound

This paper cites Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning.

Random feature approximation for general spectral methods Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:56.577248Z

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Observation c728326a-56f4-473c-9bb6-59d64ba822fb · outbound

This paper cites A stochastic gradient method with an exponential convergence \_rate for finite training sets.

Random feature approximation for general spectral methods A stochastic gradient method with an exponential convergence \_rate for finite training sets

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:56.343644Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 89f03f05-8dec-4ec1-9fce-656ea11f7cb6 · outbound

This paper cites Generalization properties of learning with random features, 2016.

Random feature approximation for general spectral methods Generalization properties of learning with random features, 2016

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:56.177208Z

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Observation e346fc1a-57e2-4e79-a509-a8bae3a1a649 · outbound

This paper cites Less is more: Nystroem computational regularization, 2016.

Random feature approximation for general spectral methods Less is more: Nystroem computational regularization, 2016

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:55.996525Z

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Observation abfc4b50-cecb-4720-ba43-c092c4a225b1 · outbound

This paper cites Schoelkopf and A.

Random feature approximation for general spectral methods Schoelkopf and A

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:55.776203Z

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Observation a9171bbd-27e0-49f4-8742-f72b3151df1f · outbound

This paper cites Mathematical Statistics.

Random feature approximation for general spectral methods Mathematical Statistics

Reference 38

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Observation 25dbb9d3-2511-482c-b234-eab2e0865dbc · outbound

This paper cites Support vector machines.

Random feature approximation for general spectral methods Support vector machines

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:55.238661Z

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Observation 6cc00641-7b63-44bb-b20c-9b61ad58807d · outbound

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Random feature approximation for general spectral methods Support vector machines

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:55.032328Z

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Observation ae1ecd15-6e7f-46a2-b296-e83c7d6bcf7d · outbound

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Random feature approximation for general spectral methods Sriperumbudur

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:54.800527Z

Source-reported events for the cited work

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Observation a3996d98-a2d1-4bf1-8260-e33759668215 · outbound

This paper cites Gain with no pain: Efficiency of kernel-pca by nyström sampling.

Random feature approximation for general spectral methods Gain with no pain: Efficiency of kernel-pca by nyström sampling

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:54.664187Z

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Observation d24dcf7b-b8b5-4af7-b329-a5bd7a3b9a56 · outbound

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Random feature approximation for general spectral methods Unresolved cited work

Reference 43

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Observation daa71454-09f5-40de-81df-c794477f4dc3 · outbound

This paper cites Using the nystr\" o m method to speed up kernel machines.

Random feature approximation for general spectral methods Using the nystr\" o m method to speed up kernel machines

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:54.460981Z

Source-reported events for the cited work

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Observation 659c7e84-a1ba-4892-903a-347006bda284 · outbound

This paper cites A proximal stochastic gradient method with progressive variance reduction.

Random feature approximation for general spectral methods A proximal stochastic gradient method with progressive variance reduction

Reference 45

Resolution: verified exact; doi, observed 2026-08-06T23:54:53.458849Z

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Observation 69dc9f0c-2a97-499c-82f4-7ff28a76aa80 · outbound

This paper cites On the Optimality of Misspecified Spectral Algorithms.

Random feature approximation for general spectral methods On the Optimality of Misspecified Spectral Algorithms

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T23:54:53.239350Z

Source-reported events for the cited work

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Observation 2f120cd7-047a-4983-bcdd-7f7786ce9300 · outbound

This paper cites Learning to learn kernels with variational random features, 2020.

Random feature approximation for general spectral methods Learning to learn kernels with variational random features, 2020

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:54:54.179333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

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