Paper Citation Record · LEDGER

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks

As of 10 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 3 inbound Pith citation observations for arXiv:2601.22409.

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

pith.paper-citation-record.v1
2601.22409 v3

Coverage vector

measured 73 of 73 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-16T09:34:00.409455Z

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One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T12:16:44.426478Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-02T16:07:08.511499Z

Reference resolution

73 of 73 outbound references displayed

verified exact5
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ac0fc6b1-669f-43c6-946e-c8a06bcae21d · outbound

This paper cites Deep learning with differential privacy.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Deep learning with differential privacy

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.078529Z

Source-reported events for the cited work

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Observation fd534ad5-a13e-4377-91eb-60f435198bac · outbound

This paper cites A convergence theory for deep learning via over- parameterization.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks A convergence theory for deep learning via over- parameterization

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.095403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ea7f3152-42ec-4bfb-933e-c4aa19a918d8 · outbound

This paper cites On functions of three variables.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks On functions of three variables

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.147274Z

Source-reported events for the cited work

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Observation ca69f3b6-2e73-438b-9a11-f9c5ee5487ab · outbound

This paper cites Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.066909Z

Source-reported events for the cited work

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Observation b682d088-9b6c-4a70-8c97-fe8b871fa15d · outbound

This paper cites Spectrally-normalized margin bounds for neural networks.Advances in Neural Information Processing Systems, 30.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Spectrally-normalized margin bounds for neural networks.Advances in Neural Information Processing Systems, 30

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.101964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9e7ca4db-87ad-40b7-9ad3-5546881fee8e · outbound

This paper cites Private stochastic convex optimization with optimal rates.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Private stochastic convex optimization with optimal rates

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.183073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 47837a76-f811-4fb4-b210-448e4d83e4d4 · outbound

This paper cites Differentially private stochastic optimization: New results in convex and non-convex settings.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Differentially private stochastic optimization: New results in convex and non-convex settings

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.209426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ed4875cf-f11a-4e11-a7dc-adecc2694444 · outbound

This paper cites Convolutional Kolmogorov-Arnold Networks.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Convolutional Kolmogorov-Arnold Networks

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:37:41.898196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3a819f6a-d280-455e-875d-77ff7d97b807 · outbound

This paper cites On a constructive proof of kolmogorov’s superposition theorem.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks On a constructive proof of kolmogorov’s superposition theorem

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.200562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 213a7a6b-671d-458e-a4e8-a280d278d7a0 · outbound

This paper cites On the convergence and calibration of deep learning with differential privacy.Transactions on machine learning research, 2023:https–openreview.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks On the convergence and calibration of deep learning with differential privacy.Transactions on machine learning research, 2023:https–openreview

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.093194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 397ef064-5176-4a29-bc60-41f0021ae8a7 · outbound

This paper cites Generalization bounds of stochastic gradient descent for wide and deep neural networks.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Generalization bounds of stochastic gradient descent for wide and deep neural networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.108623Z

Source-reported events for the cited work

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Observation 713c0616-ee78-4d7b-a1b8-af05abfb2115 · outbound

This paper cites How much over-parameterization is sufficient to learn deep relu networks? InInternational Conference on Learning Representation.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks How much over-parameterization is sufficient to learn deep relu networks? InInternational Conference on Learning Representation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.080936Z

Source-reported events for the cited work

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Observation e0937f15-f9ac-4c35-ae8e-35db5f521a81 · outbound

This paper cites Poptsova.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Poptsova

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.144752Z

Source-reported events for the cited work

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Observation 7cecbdca-e173-4148-a022-11246eb48e46 · outbound

This paper cites The MNIST database of handwritten digit images for machine learning research.IEEE Signal Processing Magazine, 29(6):141–142.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks The MNIST database of handwritten digit images for machine learning research.IEEE Signal Processing Magazine, 29(6):141–142

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.118937Z

Source-reported events for the cited work

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Observation 2ac01eaf-e2f4-44d1-8307-da372dfbf0f8 · outbound

This paper cites Gradient descent finds global minima of deep neural networks.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Gradient descent finds global minima of deep neural networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.136191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 71eb2418-c181-47ef-98e8-740cfa6e3221 · outbound

This paper cites Calibrating noise to sensitivity in private data analysis.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Calibrating noise to sensitivity in private data analysis

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.180927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 26646c2d-6547-48d0-947c-8e1b4d4e889d · outbound

This paper cites The algorithmic foundations of differential privacy.Foundations and Trends®in Theoretical Computer Science, 9(3–4):211–407.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks The algorithmic foundations of differential privacy.Foundations and Trends®in Theoretical Computer Science, 9(3–4):211–407

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.126918Z

Source-reported events for the cited work

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Observation f405f4e0-ff1e-4dcf-bbb5-c8906b70e481 · outbound

This paper cites Kan we improve on hep classification tasks? kolmogorov–arnold networks applied to an lhc physics example.Computing and Software for Big Science, 9(1):9.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Kan we improve on hep classification tasks? kolmogorov–arnold networks applied to an lhc physics example.Computing and Software for Big Science, 9(1):9

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.179001Z

Source-reported events for the cited work

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Observation 79870c22-b1fd-4aa2-97d2-78e540e094b4 · outbound

This paper cites Kanice: Kolmogorov-arnold networks with interactive convolutional elements.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Kanice: Kolmogorov-arnold networks with interactive convolutional elements

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.076133Z

Source-reported events for the cited work

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Observation 4e04f7af-c487-4cdb-861a-a30d8f09ac8d · outbound

This paper cites Random feature amplification: Feature learning and generalization in neural networks.Journal of Machine Learning Research, 24(303):1–49.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Random feature amplification: Feature learning and generalization in neural networks.Journal of Machine Learning Research, 24(303):1–49

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.121478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b399f294-169d-4311-ba82-78d8f6236e45 · outbound

This paper cites On the convergence of (stochastic) gradient descent for kolmogorov– arnold networks.IEEE Transactions on Information Theory.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks On the convergence of (stochastic) gradient descent for kolmogorov– arnold networks.IEEE Transactions on Information Theory

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.113391Z

Source-reported events for the cited work

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Observation 099c2980-f851-4210-b2c8-678cd39450d9 · outbound

This paper cites A Temporal Kolmogorov-Arnold Transformer for Time Series Forecasting.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks A Temporal Kolmogorov-Arnold Transformer for Time Series Forecasting

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T09:37:41.902465Z

Source-reported events for the cited work

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Observation 67f16b3d-c195-45b2-b126-1bbf74715f75 · outbound

This paper cites Neural tangent kernel: Convergence and generaliza- tion in neural networks.Advances in Neural Information Processing Systems, 31.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Neural tangent kernel: Convergence and generaliza- tion in neural networks.Advances in Neural Information Processing Systems, 31

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.124211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 46a3c3cc-8b31-4f89-bd0d-1deb6bb0f9a0 · outbound

This paper cites Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.099775Z

Source-reported events for the cited work

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Observation a372b194-10d1-428e-bdc2-e9f2b55e3520 · outbound

This paper cites an unresolved cited work.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-05-16T09:52:42.222018Z

Source-reported events for the cited work

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Observation 19327125-b764-46de-8b9d-890673a86394 · outbound

This paper cites Stability and generalization analysis of gradient methods for shallow neural networks.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Stability and generalization analysis of gradient methods for shallow neural networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.090761Z

Source-reported events for the cited work

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Observation 861e1f41-e1df-4c31-a1f8-a6e2baef0dd1 · outbound

This paper cites Fine-grained analysis of stability and generalization for stochastic gradient descent.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Fine-grained analysis of stability and generalization for stochastic gradient descent

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.104106Z

Source-reported events for the cited work

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Observation c127d226-7745-4915-bc7b-ea3ee9dde625 · outbound

This paper cites Kolmogorov–arnold graph neural networks for molecular property prediction.Nature Machine Intelligence, 7:1346–1354, 08 2025.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Kolmogorov–arnold graph neural networks for molecular property prediction.Nature Machine Intelligence, 7:1346–1354, 08 2025

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.142780Z

Source-reported events for the cited work

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Observation dcf92e30-d272-4cc2-a354-222dc1f6d926 · outbound

This paper cites Generalization bounds for kolmogorov- arnold networks (kans) and enhanced kans with lower lipschitz complexity.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Generalization bounds for kolmogorov- arnold networks (kans) and enhanced kans with lower lipschitz complexity

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.193326Z

Source-reported events for the cited work

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Observation 68f036c3-db79-4c60-bd48-1125364bc01c · outbound

This paper cites On the Rate of Convergence of Kolmogorov-Arnold Network Regression Estimators.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks On the Rate of Convergence of Kolmogorov-Arnold Network Regression Estimators

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-07-31T02:03:00.293982Z

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Observation 737ff09d-97a4-4f0a-bd77-5b82484a17b1 · outbound

This paper cites Kan: Kolmogorov-arnold networks.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Kan: Kolmogorov-arnold networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.217385Z

Source-reported events for the cited work

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Observation c65de89a-245e-4b26-8043-bfe44115ab7b · outbound

This paper cites How to make the gradients small privately: Improved rates for differentially private non-convex optimization.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks How to make the gradients small privately: Improved rates for differentially private non-convex optimization

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.083338Z

Source-reported events for the cited work

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Observation 92b20790-f2ca-456e-a6c0-39b1fcae6d05 · outbound

This paper cites R´ enyi differential privacy.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks R´ enyi differential privacy

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.106274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 49062ed0-cb55-4764-8b77-6ded77e2f443 · outbound

This paper cites How many neurons do we need? a refined analysis for shallow networks trained with gradient descent.Journal of Statistical Planning and Inference, page 106169.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks How many neurons do we need? a refined analysis for shallow networks trained with gradient descent.Journal of Statistical Planning and Inference, page 106169

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.140626Z

Source-reported events for the cited work

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Observation 6dba53df-2264-4032-b059-820247e2d77d · outbound

This paper cites Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:37:41.914956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 970775ab-d3fe-4ac2-a7e8-7aa17c2611d8 · outbound

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

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Optimal rates for averaged stochastic gradient descent under neural tangent kernel regime

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.149567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 08f02bac-75aa-49b3-97b2-3e55ba3bcd10 · outbound

This paper cites Overparameterized nonlinear learning: Gradient descent takes the shortest path? InInternational Conference on Machine Learning, pages 4951–4960.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Overparameterized nonlinear learning: Gradient descent takes the shortest path? InInternational Conference on Machine Learning, pages 4951–4960

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.154205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6393429e-6c10-4771-b8fd-08f38dd21563 · outbound

This paper cites Physics informed kolmogorov-arnold neural networks for dynamical analysis via efficient-kan and wav-kan.Journal of Machine Learning Research, 26(233):1–39.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Physics informed kolmogorov-arnold neural networks for dynamical analysis via efficient-kan and wav-kan.Journal of Machine Learning Research, 26(233):1–39

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.071243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 67fd5b45-ecdd-4379-ac6e-03c29b9cfd75 · outbound

This paper cites Finding local diffusion schrodinger bridge using kolmogorov-arnold network.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Finding local diffusion schrodinger bridge using kolmogorov-arnold network

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.219775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 106ba418-0ff1-4def-ac35-5eaa346e815a · outbound

This paper cites GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:37:41.919196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8add594f-0662-4cbe-bc1b-4a7621164aac · outbound

This paper cites Stability & generalisation of gradient descent for shallow neural networks without the neural tangent kernel.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Stability & generalisation of gradient descent for shallow neural networks without the neural tangent kernel

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.166811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cb75175d-905e-4b0f-9e67-ed62b0c2db93 · outbound

This paper cites Convex approximation of two-layer relu networks for hidden state differential privacy.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Convex approximation of two-layer relu networks for hidden state differential privacy

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.138282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 52be2ace-eeee-4eb1-96ef-a5880d9231e2 · outbound

This paper cites Stability vs implicit bias of gradient methods on separable data and beyond.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Stability vs implicit bias of gradient methods on separable data and beyond

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.164865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 54ab6267-76c1-4bd1-acc9-8b4150b3f875 · outbound

This paper cites Towards understanding generalization in dp-gd: A case study in training two-layer cnns.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Towards understanding generalization in dp-gd: A case study in training two-layer cnns

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.160762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2cb0cfbe-9f10-43ff-be24-9576d067199d · outbound

This paper cites an unresolved cited work.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-05-16T09:52:42.211935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e7b34884-16e5-429c-9577-cdaa8fa6a9c8 · outbound

This paper cites A survey on kolmogorov-arnold network.ACM Computing Surveys, 58(2):1–35.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks A survey on kolmogorov-arnold network.ACM Computing Surveys, 58(2):1–35

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.110910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation eee27ccd-aafc-4b8e-8121-2b0503dda13d · outbound

This paper cites Stochastic gradient descent with differentially private updates.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Stochastic gradient descent with differentially private updates

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.205867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6e200242-ba94-4725-9a27-8ca88193107a · outbound

This paper cites Kan-ddpm: Kolmogorov-arnold networks with diffusion denoising probabilistic models for mri-to-ct synthesis.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Kan-ddpm: Kolmogorov-arnold networks with diffusion denoising probabilistic models for mri-to-ct synthesis

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.185514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5d822cd8-05fc-44c0-864f-fe02745c353e · outbound

This paper cites Generalization and stability of interpolating neural networks with minimal width.Journal of Machine Learning Research, 25(156):1–41.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Generalization and stability of interpolating neural networks with minimal width.Journal of Machine Learning Research, 25(156):1–41

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.162978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dd70a752-fd68-4f61-bd2e-fd3727143a1c · outbound

This paper cites Sharper guarantees for learning neural network classifiers with gradient methods.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Sharper guarantees for learning neural network classifiers with gradient methods

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.073706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6ec38a25-9694-4fb2-a290-e78cfe35d668 · outbound

This paper cites Kolmogorov-arnold networks (kans) for time series analysis.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Kolmogorov-arnold networks (kans) for time series analysis

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:37:41.910943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 16f968e4-7d40-400e-be09-0e100db8d989 · outbound

This paper cites Cambridge university press.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Cambridge university press

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.116779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 434ea447-d3bc-42d9-a204-aa0b695eacb0 · outbound

This paper cites Cambridge university press.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Cambridge university press

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.168620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation be5d6442-cc8c-46fe-9fc5-b933e09e2b35 · outbound

This paper cites Differentially private empirical risk minimization with non-convex loss functions.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Differentially private empirical risk minimization with non-convex loss functions

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.195744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 742a6f20-263f-4cec-bbf2-7ba4a1af9246 · outbound

This paper cites Optimal utility bounds for differentially private gradient descent in three-layer neural networks.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Optimal utility bounds for differentially private gradient descent in three-layer neural networks

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.214629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e24d8f26-4b07-44d7-b3bb-faf104fdc6ff · outbound

This paper cites Generalization guarantees of gradient descent for shallow neural networks.Neural Computation, 37(2):344–402.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Generalization guarantees of gradient descent for shallow neural networks.Neural Computation, 37(2):344–402

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.156494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 64c63eb1-ef50-4933-930d-fa205f2b88a5 · outbound

This paper cites On the expressiveness and spectral bias of kans.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks On the expressiveness and spectral bias of kans

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.088450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8e97bf44-1f32-4958-86f0-e3197086ee50 · outbound

This paper cites Kolmogorov–arnold-informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on kolmogorov–arnold networks.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Kolmogorov–arnold-informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on kolmogorov–arnold networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.152021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 48523ba6-c387-4408-86d1-22a2f34f989c · outbound

This paper cites A conditional kan diffusion network for human activity recognition with missing sensor signal series.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks A conditional kan diffusion network for human activity recognition with missing sensor signal series

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.097571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a98f590c-5738-4fc3-a5ea-1afeb6e99a15 · outbound

This paper cites Efficient private erm for smooth objectives.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Efficient private erm for smooth objectives

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.198058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1ae61529-86c7-464f-aeb0-9b630b2777a7 · outbound

This paper cites Generalization bounds and model complexity for kolmogorov-arnold networks.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Generalization bounds and model complexity for kolmogorov-arnold networks

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.174434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2a97004d-bfbd-4fd2-8135-889d70e54c24 · outbound

This paper cites with probability at least 1−δ.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks with probability at least 1−δ

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.190258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f9872b5d-85a0-4c52-b487-edb00d1b7ed1 · outbound

This paper cites λmin ∇2LS(Θα,t) ≥ − cmax√m LS(Θα,t).(19) wherec max =C σ,b p 3 2 p log( m δ ) + √p+ 3 Θ(0)−Θ ∗ 2.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks λmin ∇2LS(Θα,t) ≥ − cmax√m LS(Θα,t).(19) wherec max =C σ,b p 3 2 p log( m δ ) + √p+ 3 Θ(0)−Θ ∗ 2

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.202791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 81f408fe-33ea-4693-89fe-e43d639c4404 · outbound

This paper cites an unresolved cited work.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-05-16T09:52:42.134079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 88681a79-2e79-4cae-b426-1002023d82a4 · outbound

This paper cites with probability at least 1 −δ.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks with probability at least 1 −δ

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.085854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 393c8e7c-a239-42db-87e4-88733849e826 · outbound

This paper cites with probability at least 1−δ.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks with probability at least 1−δ

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.129202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 170887eb-9d1f-4bf0-bf73-a8a83243cb38 · outbound

This paper cites Then, it holds that ES L(Θ(T))− L S(Θ(T)) ≤C σ,bp2 p+p∥Θ ∗ −Θ(0)∥ 2 2 η n ES h TX t=0 LS Θ(t) i.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Then, it holds that ES L(Θ(T))− L S(Θ(T)) ≤C σ,bp2 p+p∥Θ ∗ −Θ(0)∥ 2 2 η n ES h TX t=0 LS Θ(t) i

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.068846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1d8329da-a25c-4bf7-a503-25b67c327325 · outbound

This paper cites By further noting that (7) holds with probability at least 1 −δ over initialization c(0), this completes the proof of the theorem.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks By further noting that (7) holds with probability at least 1 −δ over initialization c(0), this completes the proof of the theorem

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.131973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 387f05b8-16ee-4c36-97a9-7741473a1be1 · outbound

This paper cites Note Lemma A.2 implies ∥c(0)∥2 ≤ 4√pm + 2 p log(2/δ) with probability at least 1 −δ/ 2.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Note Lemma A.2 implies ∥c(0)∥2 ≤ 4√pm + 2 p log(2/δ) with probability at least 1 −δ/ 2

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.188005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ff68941b-0c1f-477e-b16a-8640575e47d7 · outbound

This paper cites Combining (37) and the above equality and dividing byηyield g,Θ ∗ −Θ + ≥ 1 2η ∥Θ+ −Θ ∗∥2 2 +∥Θ−Θ +∥2 2 − ∥Θ−Θ ∗∥2 2.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks Combining (37) and the above equality and dividing byηyield g,Θ ∗ −Θ + ≥ 1 2η ∥Θ+ −Θ ∗∥2 2 +∥Θ−Θ +∥2 2 − ∥Θ−Θ ∗∥2 2

Reference 70

Resolution: malformed identifier; raw_fallback, observed 2026-05-16T09:52:42.158695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0d0c0885-6c30-4b1d-85fc-2e4748113850 · outbound

This paper cites By further using eΛ2 Θ∗ ≥ηL S(eΘ(0)), it holds 1 T TX k=1 EA LS(eΘ(k)) ≤4L S(Θ∗) + 4 ηT ∥eΘ(0)−Θ ∗∥2 2 + mp4ηT dlog(2T /δ) n2ϵ2.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks By further using eΛ2 Θ∗ ≥ηL S(eΘ(0)), it holds 1 T TX k=1 EA LS(eΘ(k)) ≤4L S(Θ∗) + 4 ηT ∥eΘ(0)−Θ ∗∥2 2 + mp4ηT dlog(2T /δ) n2ϵ2

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.176870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation db10ea31-9b0e-4631-b04f-4cfacdeb66bb · outbound

This paper cites These match the width conditions stated in the theorem.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks These match the width conditions stated in the theorem

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.170538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c97cae7-198b-456b-a0d3-2c9b7630ffbe · outbound

This paper cites This completes the proof of the theorem.

Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks This completes the proof of the theorem

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:52:42.172323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 9400ffbd-034b-4d8e-9850-df0d1e20b773 · inbound

Optimal Rates for Generalization of Gradient Descent Methods with Deep Neural Networks cites this paper.

Optimal Rates for Generalization of Gradient Descent Methods with Deep Neural Networks Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-07-02T15:57:07.404045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7d6f61fc-4b7f-4108-86f9-d57ce599eb7b · inbound

Minimax-Optimal Generalization Bounds for Smooth Deep Neural Networks Trained by (Stochastic) Gradient Descent cites this paper.

Minimax-Optimal Generalization Bounds for Smooth Deep Neural Networks Trained by (Stochastic) Gradient Descent Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-07-02T16:07:08.512676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c1567103-02cb-460b-a37d-a13222c32d1a · inbound

Minimax-Optimal Generalization Bounds for Smooth Deep Neural Networks Trained by (Stochastic) Gradient Descent cites this paper.

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-02T12:16:44.426478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.