Paper Citation Record · LEDGER

Joint learning of a network of linear dynamical systems via total variation penalization

As of 9 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2511.18737.

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pith.paper-citation-record.v1
2511.18737 v3

Coverage vector

measured 46 of 46 reference resolution

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measured 46 of 46 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

46 of 46 outbound references displayed

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Outbound references

Observation 794b941d-126d-4603-baee-65a1420f194f · outbound

This paper cites Improved algorithms for linear stochastic bandits.

Joint learning of a network of linear dynamical systems via total variation penalization Improved algorithms for linear stochastic bandits

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:01.924377Z

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Observation 7d8adf75-c998-45b9-9fd6-d78065639dfb · outbound

This paper cites Regularized estimation in sparse high-dimensional time series models.The Annals of Statistics, 43(4):1535–1567, 2015.

Joint learning of a network of linear dynamical systems via total variation penalization Regularized estimation in sparse high-dimensional time series models.The Annals of Statistics, 43(4):1535–1567, 2015

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:02.023647Z

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Observation 6e380750-1a71-4a6e-b933-7d92884da551 · outbound

This paper cites Network granger causality with inherent grouping structure.Journal of Machine Learning Research, 16(13):417–453, 2015.

Joint learning of a network of linear dynamical systems via total variation penalization Network granger causality with inherent grouping structure.Journal of Machine Learning Research, 16(13):417–453, 2015

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:02.146224Z

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Observation 93523de5-1fc8-4a68-9fa5-278d59a0982c · outbound

This paper cites Linearized aerodynamic and control law models of the x-29a airplane and comparison with flight data.National Aeronautics and Space Administration, Office of Management.

Joint learning of a network of linear dynamical systems via total variation penalization Linearized aerodynamic and control law models of the x-29a airplane and comparison with flight data.National Aeronautics and Space Administration, Office of Management

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:02.231969Z

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Observation 502f1498-2284-45c3-acaf-66502bcc412a · outbound

This paper cites Oxford University Press, 2013.

Joint learning of a network of linear dynamical systems via total variation penalization Oxford University Press, 2013

Reference 5

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Observation 6c06681e-4c18-4e9e-a7de-575d87461bec · outbound

This paper cites Campi and E.

Joint learning of a network of linear dynamical systems via total variation penalization Campi and E

Reference 6

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Observation 0c3445a4-63fb-4ec5-b47e-424dddefe47a · outbound

This paper cites Ospina, Fabio Pasqualetti, and Emiliano Dall’Anese.

Joint learning of a network of linear dynamical systems via total variation penalization Ospina, Fabio Pasqualetti, and Emiliano Dall’Anese

Reference 7

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Observation 3c9b3800-f083-44b2-ba32-1300fbf5181c · outbound

This paper cites Cormen, Charles E.

Joint learning of a network of linear dynamical systems via total variation penalization Cormen, Charles E

Reference 8

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Observation 575e57f5-f344-4a49-aa15-7cafddf162ff · outbound

This paper cites Learning sparse dynamical systems from a single sample trajectory.

Joint learning of a network of linear dynamical systems via total variation penalization Learning sparse dynamical systems from a single sample trajectory

Reference 9

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Observation ab780084-e1ef-4372-a4a6-45945cfce915 · outbound

This paper cites Telesford, Alfred B.

Joint learning of a network of linear dynamical systems via total variation penalization Telesford, Alfred B

Reference 10

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Observation ee4c56d0-e881-4685-98ac-6eff9d8b00fa · outbound

This paper cites A tail inequality for quadratic forms of subgaus- sian random vectors.Electronic Communications in Probability, 17:1–6, 2012.

Joint learning of a network of linear dynamical systems via total variation penalization A tail inequality for quadratic forms of subgaus- sian random vectors.Electronic Communications in Probability, 17:1–6, 2012

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:02.685000Z

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Observation fe7356ad-c188-427c-a448-3a14ea541769 · outbound

This paper cites Optimal rates for total variation denoising.

Joint learning of a network of linear dynamical systems via total variation penalization Optimal rates for total variation denoising

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:02.742236Z

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Observation bd843fa0-5e4e-474e-8276-7bcaa4a11799 · outbound

This paper cites Finite-time identification of stable linear systems opti- mality of the least-squares estimator.

Joint learning of a network of linear dynamical systems via total variation penalization Finite-time identification of stable linear systems opti- mality of the least-squares estimator

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:02.774097Z

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Observation 93257be7-526e-4082-8c1c-fa132f0fe93b · outbound

This paper cites Oracle inequalities for high dimensional vector autoregressions.Journal of Econometrics, 186(2):325–344, 2015.

Joint learning of a network of linear dynamical systems via total variation penalization Oracle inequalities for high dimensional vector autoregressions.Journal of Econometrics, 186(2):325–344, 2015

Reference 14

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Observation b645bd97-999f-4d46-b773-8600f6da59d1 · outbound

This paper cites Suprema of chaos processes and the restricted isometry property.Communications on Pure and Applied Mathematics, 67(11):1877– 1904, 2014.

Joint learning of a network of linear dynamical systems via total variation penalization Suprema of chaos processes and the restricted isometry property.Communications on Pure and Applied Mathematics, 67(11):1877– 1904, 2014

Reference 15

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Observation f1a6045f-132d-4b0f-8dea-d276cb593368 · outbound

This paper cites Asymptotic properties of general autoregressive models and strong consistency of least-squares estimates of their parameters.Journal of Multivariate Analysis, 13(1):1–23, 1983.

Joint learning of a network of linear dynamical systems via total variation penalization Asymptotic properties of general autoregressive models and strong consistency of least-squares estimates of their parameters.Journal of Multivariate Analysis, 13(1):1–23, 1983

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:02.927831Z

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Observation c6b3fc7c-0767-46ae-9a03-575552ba5648 · outbound

This paper cites Extended least squares and their applications to adaptive control and prediction in linear systems.IEEE Transactions on Automatic Control, 31(10):898–906, 1986.

Joint learning of a network of linear dynamical systems via total variation penalization Extended least squares and their applications to adaptive control and prediction in linear systems.IEEE Transactions on Automatic Control, 31(10):898–906, 1986

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:02.982870Z

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Observation 7eac6bbe-b7af-498c-9a17-84be690521d0 · outbound

This paper cites Least Squares Estimates in Stochastic Regression Models with Applications to Identification and Control of Dynamic Systems.The Annals of Statistics, 10(1):154 – 166, 1982.

Joint learning of a network of linear dynamical systems via total variation penalization Least Squares Estimates in Stochastic Regression Models with Applications to Identification and Control of Dynamic Systems.The Annals of Statistics, 10(1):154 – 166, 1982

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:03.076014Z

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Observation 82089b0a-bbdc-4611-96b2-3de22e6f22af · outbound

This paper cites Variable fusion: A new adaptive signal regression method.Dept.

Joint learning of a network of linear dynamical systems via total variation penalization Variable fusion: A new adaptive signal regression method.Dept

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:03.088557Z

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Observation d9f6159e-b56d-43f8-918c-1942b11838f5 · outbound

This paper cites Graph-based regularization for regression problems with alignment and highly corre- lated designs.SIAM Journal on Mathematics of Data Science, 2(2):480–504, 2020.

Joint learning of a network of linear dynamical systems via total variation penalization Graph-based regularization for regression problems with alignment and highly corre- lated designs.SIAM Journal on Mathematics of Data Science, 2(2):480–504, 2020

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:03.140060Z

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Observation 10c377b3-5034-4285-abe3-6ac4c54295fb · outbound

This paper cites Wainwright.

Joint learning of a network of linear dynamical systems via total variation penalization Wainwright

Reference 21

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Observation 0878da73-7668-4795-ac95-2f95a200a654 · outbound

This paper cites Estimating structured vector autoregressive models.

Joint learning of a network of linear dynamical systems via total variation penalization Estimating structured vector autoregressive models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:03.183512Z

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Observation 941baf9f-fc8d-47f5-98f6-241bc4c1c9db · outbound

This paper cites Joint learning of linear time-invariant dynamical systems.Automatica, 164:111635, 2024.

Joint learning of a network of linear dynamical systems via total variation penalization Joint learning of linear time-invariant dynamical systems.Automatica, 164:111635, 2024

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:03.211247Z

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Observation 4ce1a455-9546-4652-a919-5958a3d9d0ef · outbound

This paper cites Prediction bounds for higher order total variation regularized least squares.The Annals of Statistics, 49(5):2755–2773, 2021.

Joint learning of a network of linear dynamical systems via total variation penalization Prediction bounds for higher order total variation regularized least squares.The Annals of Statistics, 49(5):2755–2773, 2021

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:03.220718Z

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Observation 7ffffb87-708d-4d7b-a189-bf0678c4d0b5 · outbound

This paper cites Wainwright, and Bin Yu.

Joint learning of a network of linear dynamical systems via total variation penalization Wainwright, and Bin Yu

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:03.269758Z

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Observation 5c99f3a4-85f7-4a6e-929f-167d80de8d9f · outbound

This paper cites Total variation classes beyond 1d: Minimax rates, and the limitations of linear smoothers.Advances in Neural Information Processing Systems, 29:3521–3529, 2016.

Joint learning of a network of linear dynamical systems via total variation penalization Total variation classes beyond 1d: Minimax rates, and the limitations of linear smoothers.Advances in Neural Information Processing Systems, 29:3521–3529, 2016

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:03.343645Z

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Observation 587462a2-4f18-4dbb-a362-6d2097d98e27 · outbound

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

Joint learning of a network of linear dynamical systems via total variation penalization Near optimal finite time identification of arbitrary linear dynamical systems

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:03.402063Z

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Observation 08ec994b-19f3-40d4-8f4f-4d223da868e9 · outbound

This paper cites Finite time identification in unstable linear systems.Automatica, 96:342–353, 2018.

Joint learning of a network of linear dynamical systems via total variation penalization Finite time identification in unstable linear systems.Automatica, 96:342–353, 2018

Reference 28

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Observation 3f80f52f-cfbb-4b68-9ead-1aa7bfbcf46e · outbound

This paper cites Jordan, and Benjamin Recht.

Joint learning of a network of linear dynamical systems via total variation penalization Jordan, and Benjamin Recht

Reference 29

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Observation 9af783aa-162b-4c80-a2cb-046bf6f476c0 · outbound

This paper cites Kim, Harang Ju, Dale Zhou, Cassiano Becker, Fabio Pasqualetti, George J.

Joint learning of a network of linear dynamical systems via total variation penalization Kim, Harang Ju, Dale Zhou, Cassiano Becker, Fabio Pasqualetti, George J

Reference 30

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Observation 50af4fa8-4eb0-4a7e-9cce-f91a280e45c3 · outbound

This paper cites Wellesley-Cambridge Press, Philadel- phia, PA, 2007.

Joint learning of a network of linear dynamical systems via total variation penalization Wellesley-Cambridge Press, Philadel- phia, PA, 2007

Reference 31

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Joint learning of a network of linear dynamical systems via total variation penalization Springer, 2005

Reference 32

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Joint learning of a network of linear dynamical systems via total variation penalization Springer, 2014

Reference 33

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Joint learning of a network of linear dynamical systems via total variation penalization Stanford University, 2011

Reference 34

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Observation 3b111177-7d1f-4dc6-b27e-8234fa3cc34c · outbound

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Joint learning of a network of linear dynamical systems via total variation penalization Unresolved cited work

Reference 35

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Observation e64d9c8b-9d87-45ea-aeec-e62c7100f0ad · outbound

This paper cites Joint estimation of smooth graph signals from partial linear measurements.

Joint learning of a network of linear dynamical systems via total variation penalization Joint estimation of smooth graph signals from partial linear measurements

Reference 36

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Joint learning of a network of linear dynamical systems via total variation penalization Joint learning of linear dynamical systems under smoothness constraints

Reference 37

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Joint learning of a network of linear dynamical systems via total variation penalization Learning linear dynamical systems under convex constraints

Reference 38

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Joint learning of a network of linear dynamical systems via total variation penalization Cambridge University Press, 2025

Reference 39

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Joint learning of a network of linear dynamical systems via total variation penalization Vidyasagar and R.L

Reference 40

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Observation fff1a4a4-e73c-4769-b449-73c8506c3b6c · outbound

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Joint learning of a network of linear dynamical systems via total variation penalization Unresolved cited work

Reference 41

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This paper cites Fedsysid: A federated approach to sample-efficient system identification.

Joint learning of a network of linear dynamical systems via total variation penalization Fedsysid: A federated approach to sample-efficient system identification

Reference 42

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This paper cites Trend filtering on graphs.Journal of Machine Learning Research, 17(105):1–41, 2016.

Joint learning of a network of linear dynamical systems via total variation penalization Trend filtering on graphs.Journal of Machine Learning Research, 17(105):1–41, 2016

Reference 43

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Observation a51dddcd-24a8-43c6-90b5-59c0c44af3f9 · outbound

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

Joint learning of a network of linear dynamical systems via total variation penalization Learning the dynamics of autonomous linear systems from multiple trajectories

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:05.716930Z

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Observation 0df65d8a-d867-465b-aa7f-bf75aaf2e88a · outbound

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Joint learning of a network of linear dynamical systems via total variation penalization Chiu, and Shreyas Sundaram

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:05.806642Z

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Observation 5fd047c3-b5d3-44ca-8ada-34eba6089ab3 · outbound

This paper cites Non-asymptotic identification of linear dynamical systems using multiple trajectories.IEEE Control Systems Letters, 5(5):1693–1698, 2020.

Joint learning of a network of linear dynamical systems via total variation penalization Non-asymptotic identification of linear dynamical systems using multiple trajectories.IEEE Control Systems Letters, 5(5):1693–1698, 2020

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-03T20:49:05.860034Z

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