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Paper Citation Record · LEDGER

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach

As of 9 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2607.17595.

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pith.paper-citation-record.v1
2607.17595 v1

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measured 29 of 29 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-01T17:43:48.999241Z

measured 29 of 29 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

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Reference resolution

29 of 29 outbound references displayed

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Outbound references

Observation e45e8c49-2347-4ab6-8ea1-10dd11a80226 · outbound

This paper cites Learning algorithms for markov decision processes with average cost.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Learning algorithms for markov decision processes with average cost

Reference 1

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Observation 7c28c3be-d63b-4035-889f-f936b4858d6e · outbound

This paper cites Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise

Reference 2

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Observation 5983a74b-a792-46af-adbd-a2579a163445 · outbound

This paper cites First-Order Methods in Optimization.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach First-Order Methods in Optimization

Reference 3

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Observation 7d63f2aa-e7b7-4105-a040-ce8c25e22981 · outbound

This paper cites Adaptive algorithms and stochastic approximations.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Adaptive algorithms and stochastic approximations

Reference 4

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Observation 9ef0b427-b560-466e-867c-e7912ef8407a · outbound

This paper cites A finite time analysis of temporal difference learning with linear function approximation, in: Proceedings of the 31st Conference On Learning Theory, PMLR.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach A finite time analysis of temporal difference learning with linear function approximation, in: Proceedings of the 31st Conference On Learning Theory, PMLR

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This paper cites an unresolved cited work.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Unresolved cited work

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Observation 356c2409-079d-4ff7-a439-751c1784e3ee · outbound

This paper cites Stochastic fixed-point iterations for nonexpansive maps: Convergence and error bounds.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Stochastic fixed-point iterations for nonexpansive maps: Convergence and error bounds

Reference 7

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Observation 0fc3b41c-ab5c-4171-b2c7-27c0cfc10a76 · outbound

This paper cites A concentration bound for td(0) with function approximation.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach A concentration bound for td(0) with function approximation

Reference 8

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Observation d958065c-84ff-45ea-a8bb-c086eb57f57e · outbound

This paper cites Concentration of contractive stochastic approximation and reinforcement learning.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Concentration of contractive stochastic approximation and reinforcement learning

Reference 9

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Observation 95ec4154-b550-4588-a923-148328dcac77 · outbound

This paper cites Finite-timeboundsfortwo-time-scalestochasticapproximationwitharbitrarynormcontractions and markovian noise, in: 2025 IEEE 64th Conference on Decision and Control (CDC), pp.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Finite-timeboundsfortwo-time-scalestochasticapproximationwitharbitrarynormcontractions and markovian noise, in: 2025 IEEE 64th Conference on Decision and Control (CDC), pp

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Observation ae051811-7790-4898-9348-9c46d24b57e8 · outbound

This paper cites Heavy-tailedandlong-rangedependentnoiseinstochasticapproximation:Afinite-time analysis.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Heavy-tailedandlong-rangedependentnoiseinstochasticapproximation:Afinite-time analysis

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Observation 9eef61db-4769-4395-a125-ba2767c7c45e · outbound

This paper cites Non-Asymptotic Convergence of Stochastic Iterative Algorithms: A Lyapunov Framework.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Non-Asymptotic Convergence of Stochastic Iterative Algorithms: A Lyapunov Framework

Reference 12

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Observation 62b6c6b8-8ab3-40f7-856f-1123e1252960 · outbound

This paper cites Finite-sampleanalysisofcontractivestochasticapproximationusingsmooth convex envelopes, in: Advances in Neural Information Processing Systems, Curran Associates, Inc.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Finite-sampleanalysisofcontractivestochasticapproximationusingsmooth convex envelopes, in: Advances in Neural Information Processing Systems, Curran Associates, Inc

Reference 13

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Observation fef1eb9c-0720-4865-ad82-71076e3ef9ed · outbound

This paper cites A lyapunov theory for finite-sample guarantees of markovian stochastic approximation.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach A lyapunov theory for finite-sample guarantees of markovian stochastic approximation

Reference 14

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Observation 4d8d847e-6add-48f5-ae57-af77eed15f65 · outbound

This paper cites Concentration of contractive stochastic approximation: Additive and multiplicative noise.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Concentration of contractive stochastic approximation: Additive and multiplicative noise

Reference 15

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Observation cfe74706-1849-4889-8b41-3d276ce0c6d2 · outbound

This paper cites Finite sample analyses for TD(0) with function approximation, in: Proceedings of the AAAI Conference on Artificial Intelligence.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Finite sample analyses for TD(0) with function approximation, in: Proceedings of the AAAI Conference on Artificial Intelligence

Reference 16

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Observation 694f4acb-d57d-40ab-aa72-a189076fa0f7 · outbound

This paper cites Tight high probability bounds for linear stochastic approximation with fixed stepsize, in: Advances in Neural Information Processing Systems, Curran Associates, Inc.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Tight high probability bounds for linear stochastic approximation with fixed stepsize, in: Advances in Neural Information Processing Systems, Curran Associates, Inc

Reference 17

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Observation cc8e2767-c38e-4f2d-9f12-be9f5fbe9b6b · outbound

This paper cites Stochastic Approximation and Recursive Algorithms and Applications.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Stochastic Approximation and Recursive Algorithms and Applications

Reference 18

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Observation 944ceaaa-8ab6-4c12-bc81-7ce493fa4f65 · outbound

This paper cites Isq-learningminimaxoptimal?atightsamplecomplexityanalysis.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Isq-learningminimaxoptimal?atightsamplecomplexityanalysis

Reference 19

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Observation d0042360-f371-4a06-8fb7-021f21b6d5bf · outbound

This paper cites Sample complexity of asynchronousQ-learning: Sharper analysisand variance reduction,in: Advances in Neural Information Processing Systems, Curran Associates, Inc.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Sample complexity of asynchronousQ-learning: Sharper analysisand variance reduction,in: Advances in Neural Information Processing Systems, Curran Associates, Inc

Reference 20

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Observation 398b42cf-391f-446d-80aa-c336f15547f5 · outbound

This paper cites Non-asymptotic analysis of stochastic approximation algorithms for machine learning, in: Advances in Neural Information Processing Systems, Curran Associates, Inc.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Non-asymptotic analysis of stochastic approximation algorithms for machine learning, in: Advances in Neural Information Processing Systems, Curran Associates, Inc

Reference 21

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Observation cd9c1d2d-8da8-4b33-86f1-bcdb00c176f8 · outbound

This paper cites Robuststochasticapproximationapproachtostochasticprogramming.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Robuststochasticapproximationapproachtostochasticprogramming

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Observation a942cd28-d270-4676-a68f-c8c27102c544 · outbound

This paper cites Time-uniform concentration bounds for iterative algorithms.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Time-uniform concentration bounds for iterative algorithms

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Observation 71b9b92d-810f-49c1-8961-216e7c228253 · outbound

This paper cites Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise

Reference 24

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Observation a8d21445-8c64-45af-86fe-d3fc5f753b87 · outbound

This paper cites Finite-time analysis of asynchronous stochastic approximation and Q-learning, in: Proceedings of Thirty Third Conference on Learning Theory, PMLR.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Finite-time analysis of asynchronous stochastic approximation and Q-learning, in: Proceedings of Thirty Third Conference on Learning Theory, PMLR

Reference 25

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Observation 74fcc5bf-fb3e-47ea-843e-c4fe9f32fa3e · outbound

This paper cites Finite-timeerrorboundsforlinearstochasticapproximationandtdlearning,in:ProceedingsoftheThirty-Second Conference on Learning Theory, PMLR.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Finite-timeerrorboundsforlinearstochasticapproximationandtdlearning,in:ProceedingsoftheThirty-Second Conference on Learning Theory, PMLR

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Observation 06acc0e9-72cd-45f9-821e-ffae71f149b8 · outbound

This paper cites A concentration bound for stochastic approximation via Alekseev’s formula.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach A concentration bound for stochastic approximation via Alekseev’s formula

Reference 27

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Observation 6aff3cfc-8277-4400-8974-030aa10f604f · outbound

This paper cites An analysis of temporal-difference learning with function approximation.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach An analysis of temporal-difference learning with function approximation

Reference 28

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Observation f22267f1-5ae2-4828-8829-5b6ae3e6ce7d · outbound

This paper cites Q-learning.

Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach Q-learning

Reference 29

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