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Paper Citation Record · LEDGER

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data

As of 23 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2505.04954.

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

pith.paper-citation-record.v1
2505.04954 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

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measured 35 of 35 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

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External citation measurements

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Outbound references

Observation 79eb01d3-789f-4c85-b402-e9a661cd35b3 · outbound

This paper cites Safely learning dynamical systems from short trajectories.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Safely learning dynamical systems from short trajectories

Reference 1

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Observation a2f38d83-d2eb-48a7-9c74-f809854f8c1b · outbound

This paper cites State space modeling of time series.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data State space modeling of time series

Reference 2

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Observation 0fb41210-08b7-4af3-a705-5aaf5758831a · outbound

This paper cites The impact of machine learning on economics.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data The impact of machine learning on economics

Reference 3

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Observation 6ffd2e32-9a3d-4901-9dd5-627908cfd1ce · outbound

This paper cites Consistency and asymptotic normality of some subspace algorithms for systems without observed inputs.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Consistency and asymptotic normality of some subspace algorithms for systems without observed inputs

Reference 4

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Observation 4f9f4be6-b4d8-4013-b772-c42364218b54 · outbound

This paper cites Hermitian matrix inequalities and a conjecture.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Hermitian matrix inequalities and a conjecture

Reference 5

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Observation 7798a434-7129-4f33-9bf2-56342c3eba37 · outbound

This paper cites Lipschitz functions.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Lipschitz functions

Reference 6

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Observation b7d40ff2-9595-446e-a7f9-6f8da1315c6f · outbound

This paper cites Introduction to calculus and analysis , volume 1.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Introduction to calculus and analysis , volume 1

Reference 7

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Source-reported events for the cited work

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Observation 1ea2742d-6180-4b2d-8c3a-4beb1577bdf1 · outbound

This paper cites On the sample complexity of the linear quadratic regulator.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data On the sample complexity of the linear quadratic regulator

Reference 8

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Observation 6d00ee35-4d6d-4893-8253-34bb50cd2cb1 · outbound

This paper cites Finite time identification in unstable linear systems.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Finite time identification in unstable linear systems

Reference 9

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This paper cites Data-driven sparse system identification.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Data-driven sparse system identification

Reference 10

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Observation e007655e-681f-4027-b74f-bdf0058dddc7 · outbound

This paper cites Higher-order derivatives and taylor’s formula in several variables.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Higher-order derivatives and taylor’s formula in several variables

Reference 11

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Observation f47afd45-2701-45a5-890a-adcb2110f90f · outbound

This paper cites Learning nonlinear dynamical systems from a single trajectory.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Learning nonlinear dynamical systems from a single trajectory

Reference 12

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Observation 2de397c6-6126-4ab9-a33d-4e9889bf0ada · outbound

This paper cites Deep koopman learning of nonlinear time-varying systems.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Deep koopman learning of nonlinear time-varying systems

Reference 13

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This paper cites Ridge regression: Biased estimation for nonorthogonal problems.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Ridge regression: Biased estimation for nonorthogonal problems

Reference 14

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Observation b480591c-fd95-493a-a8e0-59b101363ccc · outbound

This paper cites On consistency of subspace methods for system identification.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data On consistency of subspace methods for system identification

Reference 15

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Observation 46638451-78f3-4940-86de-044769ba698d · outbound

This paper cites System identification.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data System identification

Reference 16

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Source-reported events for the cited work

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Observation 76eb2ed7-fe0b-4787-8fcd-6fe8e99023b7 · outbound

This paper cites Active Learning for Nonlinear System Identification with Guarantees.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Active Learning for Nonlinear System Identification with Guarantees

Reference 17

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Source-reported events for the cited work

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Observation 3e0fd504-7a4e-4801-9214-6a91aafd37fb · outbound

This paper cites k-fold cross-validation explained in plain english (for evaluating a model’s performance and hyperparameter tuning), 2020.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data k-fold cross-validation explained in plain english (for evaluating a model’s performance and hyperparameter tuning), 2020

Reference 18

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Source-reported events for the cited work

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Observation a8819bea-b441-4c75-b501-1beba8e2b452 · outbound

This paper cites Linear identification of nonlinear systems: A lifting technique based on the koopman operator.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Linear identification of nonlinear systems: A lifting technique based on the koopman operator

Reference 19

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Source-reported events for the cited work

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Observation 7cfb5328-bed4-436b-b89a-6a85e2ae8626 · outbound

This paper cites Machine learning , volume 1.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Machine learning , volume 1

Reference 20

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Source-reported events for the cited work

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Observation 83ca869b-1cad-452d-a001-75af9d1660ba · outbound

This paper cites Non-asymptotic identification of LTI systems from a single trajectory.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Non-asymptotic identification of LTI systems from a single trajectory

Reference 21

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Observation 8b794880-04d0-4cbf-8bc3-f68fefcb503e · outbound

This paper cites Cross- validation.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Cross- validation

Reference 22

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Observation 505680e4-1e3c-456c-aa8d-5cf32d15fd13 · outbound

This paper cites Subgaussian random variables: An expository note.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Subgaussian random variables: An expository note

Reference 23

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Observation e2ca5df2-1451-4b13-ae4c-c86c7cc01e3d · outbound

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Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Linear system theory

Reference 24

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Observation 829c93eb-1f40-4da8-87ec-67d3361b67bd · outbound

This paper cites Near optimal finite time identification of arbitrary linear dynamical systems.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Near optimal finite time identification of arbitrary linear dynamical systems

Reference 25

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Observation 3fe37a11-0f0d-4f8a-8563-96d6bfa6d48c · outbound

This paper cites Nonparametric system identification of stochastic switched linear systems.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Nonparametric system identification of stochastic switched linear systems

Reference 26

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Observation 04687db7-0f21-4037-a877-04fa6af2f236 · outbound

This paper cites Accurate parameter estimation for safety-critical systems with unmodeled dynamics.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Accurate parameter estimation for safety-critical systems with unmodeled dynamics

Reference 27

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verified fuzzy
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Source-reported events for the cited work

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Observation ada1ab54-549d-4960-8ab0-19da5d02dc4f · outbound

This paper cites Non-asymptotic and accurate learning of nonlinear dynamical systems.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Non-asymptotic and accurate learning of nonlinear dynamical systems

Reference 28

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Observation 9b3f96ce-d7b1-4cdf-aad1-a344c2f78044 · outbound

This paper cites Learning linear dynamical systems with semi-parametric least squares.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Learning linear dynamical systems with semi-parametric least squares

Reference 29

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Observation 5b3f100c-ee2b-4c55-9581-329495848e0d · outbound

This paper cites Learning without mixing: Towards a sharp analysis of linear system identification.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Learning without mixing: Towards a sharp analysis of linear system identification

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation a20517a7-ad76-4fd7-bbac-4beb75a17f4f · outbound

This paper cites Learning the dynamics of autonomous linear systems from multiple trajectories.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Learning the dynamics of autonomous linear systems from multiple trajectories

Reference 31

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation f3e61f65-2b8c-4623-9f20-ddb1403eba87 · outbound

This paper cites Learning linearized models from nonlinear systems with finite data.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Learning linearized models from nonlinear systems with finite data

Reference 32

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 88923a33-3105-4adf-83b5-ab95a1cb0eb7 · outbound

This paper cites Learning Dynamical Systems by Leveraging Data from Similar Systems.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Learning Dynamical Systems by Leveraging Data from Similar Systems

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0d08dac2-b175-47bd-87d3-5ddab1280288 · outbound

This paper cites On the sample complexity of decentralized linear quadratic regulator with partially nested information structure.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data On the sample complexity of decentralized linear quadratic regulator with partially nested information structure

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation c460de25-a0a7-4a2c-a859-9c3557e97113 · outbound

This paper cites Non-asymptotic identification of linear dynamical systems using multiple trajectories.

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data Non-asymptotic identification of linear dynamical systems using multiple trajectories

Reference 35

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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