Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach | Paper Citation Record

Observation 5c871f28-7789-46fc-88e5-752831465d67 · outbound

This paper cites Improved algorithms for linear stochastic bandits.Advances in neural information processing systems, 24, 2011.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Improved algorithms for linear stochastic bandits.Advances in neural information processing systems, 24, 2011

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Observation cf06a4fa-4a43-4202-8181-31617404dc43 · outbound

This paper cites Online Least Squares Estimation with Self-Normalized Processes: An Application to Bandit Problems.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Online Least Squares Estimation with Self-Normalized Processes: An Application to Bandit Problems

Reference 2

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Observation e20b5274-f422-4fe3-85dc-e65dc2a2424d · outbound

This paper cites Associative reinforcement learning using linear probabilistic concepts.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Associative reinforcement learning using linear probabilistic concepts

Reference 3

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Observation 1a1bf47f-d21e-43ae-ae55-c9ec58210934 · outbound

This paper cites Robust adaptive mpc for constrained uncertain nonlinear systems.International Journal of Adaptive Control and Signal Processing, 25(2):155–167, 2011.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Robust adaptive mpc for constrained uncertain nonlinear systems.International Journal of Adaptive Control and Signal Processing, 25(2):155–167, 2011

Reference 4

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Observation 3f04a2c9-3ef5-41eb-ae48-e8a2ec309d02 · outbound

This paper cites Thompson sampling for contextual bandits with linear payoffs.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Thompson sampling for contextual bandits with linear payoffs

Reference 5

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This paper cites The size of the membership-set in a probabilistic framework.Automatica, 40 (2):253–260, 2004.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach The size of the membership-set in a probabilistic framework.Automatica, 40 (2):253–260, 2004

Reference 6

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Observation ef3a028d-cfeb-45a9-9788-a083186be91d · outbound

This paper cites Convergence analysis of central and minimax algorithms in scalar regressor models.Mathematics of Control, Signals and Systems, 18(1):66–99, 2006.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Convergence analysis of central and minimax algorithms in scalar regressor models.Mathematics of Control, Signals and Systems, 18(1):66–99, 2006

Reference 7

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Observation 9b13d1b2-0775-455a-9308-5d4957eb0b80 · outbound

This paper cites Using confidence bounds for exploitation-exploration trade-offs.Journal of machine learning research, 3(Nov):397–422, 2002.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Using confidence bounds for exploitation-exploration trade-offs.Journal of machine learning research, 3(Nov):397–422, 2002

Reference 8

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Observation b5a4549e-d573-40df-984b-87b2a4e9954e · outbound

This paper cites Convergence of optimal sequential outer bounding sets in bounded error parameter estimation.Mathematics and computers in simulation, 49(6):307–317, 1999.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Convergence of optimal sequential outer bounding sets in bounded error parameter estimation.Mathematics and computers in simulation, 49(6):307–317, 1999

Reference 9

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Observation 8ef63618-2e30-41cc-a622-19fc1d8f0570 · outbound

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Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Unresolved cited work

Reference 10

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Observation feeb8c90-35d8-47f8-a403-c806f6b16ce4 · outbound

This paper cites Convergence properties of the membership set.Automatica, 34(10):1245–1249, 1998.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Convergence properties of the membership set.Automatica, 34(10):1245–1249, 1998

Reference 11

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Observation 68645e28-99ef-460b-87e7-1298d60ab8c3 · outbound

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Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Unresolved cited work

Reference 12

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Observation 301f173d-f7f5-412a-bd33-5a71ab8d31c2 · outbound

This paper cites Active learning for stochastic contextual linear bandits.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Active learning for stochastic contextual linear bandits

Reference 13

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Observation 677e2841-7b49-4b47-b830-49b8e05e8b57 · outbound

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Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Unresolved cited work

Reference 14

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Observation 7438bd79-5ef2-4de2-8791-1ab8ab43e3f0 · outbound

This paper cites Thompson sampling for high- dimensional sparse linear contextual bandits.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Thompson sampling for high- dimensional sparse linear contextual bandits

Reference 15

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Observation ad558dce-630b-4127-8743-253c4d0c5ed5 · outbound

This paper cites Contextual restless multi-armed bandits with application to demand response decision-making.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Contextual restless multi-armed bandits with application to demand response decision-making

Reference 16

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Observation e289e930-b636-4eea-875d-9936617d9edd · outbound

This paper cites Online residential demand response via contextual multi- armed bandits.IEEE Control Systems Letters, 5(2):433–438, 2020.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Online residential demand response via contextual multi- armed bandits.IEEE Control Systems Letters, 5(2):433–438, 2020

Reference 17

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Observation d3e0de97-9e6e-47bf-8ece-f813432a88c7 · outbound

This paper cites Shuffle Private Linear Contextual Bandits.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Shuffle Private Linear Contextual Bandits

Reference 18

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Observation c3c5b483-5b46-4f29-a3d3-f10a172a32e7 · outbound

This paper cites Contextual bandits with linear payoff functions.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Contextual bandits with linear payoff functions

Reference 19

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Observation 964485a0-8ba7-4230-923a-7e899e4cee7b · outbound

This paper cites Balanced linear contextual bandits.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Balanced linear contextual bandits

Reference 20

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Observation 6f4684f7-9e2d-4958-a7da-91635d7f9c8c · outbound

This paper cites On the value of information in system identification—bounded noise case.Automatica, 18(2):229–238, 1982.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach On the value of information in system identification—bounded noise case.Automatica, 18(2):229–238, 1982

Reference 21

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Observation 4999a6a2-c99a-468f-9d1e-a526aabf74b9 · outbound

This paper cites Beyond ucb: Optimal and efficient contextual bandits with regression oracles.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Beyond ucb: Optimal and efficient contextual bandits with regression oracles

Reference 22

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Observation 11744ad9-2d30-4af7-8230-a1e3998139a2 · outbound

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Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Unresolved cited work

Reference 23

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Observation 6ebf9c48-fb03-4e35-88cf-bbd5befb4f92 · outbound

This paper cites Robust and adaptive model predictive control of nonlinear systems.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Robust and adaptive model predictive control of nonlinear systems

Reference 24

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Observation e858b8f7-57b0-430f-8fec-f9a4a3dd0575 · outbound

This paper cites The extremal volume ellipsoids of convex bodies, their symmetry properties, and their determination in some special cases.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach The extremal volume ellipsoids of convex bodies, their symmetry properties, and their determination in some special cases

Reference 25

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Observation f78547be-36f8-461d-a9d5-31d6b4432436 · outbound

This paper cites AdaLinUCB: Opportunistic Learning for Contextual Bandits.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach AdaLinUCB: Opportunistic Learning for Contextual Bandits

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Resolution: verified exact; local_arxiv, observed 2026-07-04T05:39:40.446704Z

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Observation 87f181ef-2619-48bf-a97a-32eb31039199 · outbound

This paper cites Contexts can be cheap: Solving stochastic contextual bandits with linear bandit algorithms.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Contexts can be cheap: Solving stochastic contextual bandits with linear bandit algorithms

Reference 27

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Observation 8d18ad30-d1e1-4da7-85f6-12665578b52d · outbound

This paper cites Nearly optimal algorithms for linear contextual bandits with adversarial corruptions.Advances in neural information processing systems, 35:34614–34625, 2022.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Nearly optimal algorithms for linear contextual bandits with adversarial corruptions.Advances in neural information processing systems, 35:34614–34625, 2022

Reference 28

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Observation f812dbf6-99e3-4f29-abb1-f92fc9ab0155 · outbound

This paper cites Random design analysis of ridge regression.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Random design analysis of ridge regression

Reference 29

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Observation 6dc36915-0c81-4942-8ad5-95bf0f283c7c · outbound

This paper cites Extremum problems with inequalities as subsidiary conditions.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Extremum problems with inequalities as subsidiary conditions

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Observation 388270ac-6c09-4989-acd7-c285788c9c45 · outbound

This paper cites A smoothed analysis of the greedy algorithm for the linear contextual bandit problem.Advances in neural information processing systems, 31, 2018.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach A smoothed analysis of the greedy algorithm for the linear contextual bandit problem.Advances in neural information processing systems, 31, 2018

Reference 31

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Observation c7a064c0-719e-43b5-9efb-105a847c4f39 · outbound

This paper cites Conservative contextual linear bandits.Advances in neural information processing systems, 30, 2017.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Conservative contextual linear bandits.Advances in neural information processing systems, 30, 2017

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Observation 22d8ba7f-7ee7-46cf-8bae-a1388ca04c4a · outbound

This paper cites Contextual linear bandits under noisy features: Towards bayesian oracles.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Contextual linear bandits under noisy features: Towards bayesian oracles

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Observation f3054506-6c1c-4442-84c9-bef94eb13ad1 · outbound

This paper cites Doubly robust thompson sampling with linear payoffs.Advances in neural information processing systems, 34:15830–15840, 2021.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Doubly robust thompson sampling with linear payoffs.Advances in neural information processing systems, 34:15830–15840, 2021

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Observation e2ca5c05-0c22-4d50-a92c-8e2b911c86ed · outbound

This paper cites Efficient linear bandits through matrix sketching.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Efficient linear bandits through matrix sketching

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Observation cc7ccfd1-6e2b-4612-984a-46c8b8017e3d · outbound

This paper cites Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems.The Annals of Statistics, pages 154–166, 1982.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems.The Annals of Statistics, pages 154–166, 1982

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Observation 6e769d18-7f38-4260-8995-1473fdb72f02 · outbound

This paper cites An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions

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Observation 46588f8f-6f68-472e-ac65-ee77386060cc · outbound

This paper cites A contextual-bandit approach to personalized news article recommendation.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach A contextual-bandit approach to personalized news article recommendation

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Observation 519e2c4a-01db-4748-863b-c3d168bafb8e · outbound

This paper cites Nearly minimax-optimal regret for linearly parame- terized bandits.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Nearly minimax-optimal regret for linearly parame- terized bandits

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Observation 0cffc272-1ac6-48a3-b241-d9abfba2be7e · outbound

This paper cites Tight regret bounds for infinite-armed linear contextual bandits.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Tight regret bounds for infinite-armed linear contextual bandits

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Observation d6685549-2700-42c3-8e18-23771acce32e · outbound

This paper cites Learning the uncertainty sets of linear control systems via set membership: A non-asymptotic analysis.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Learning the uncertainty sets of linear control systems via set membership: A non-asymptotic analysis

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Observation 20c6c691-e37c-4041-9de2-cd04f3e5faa1 · outbound

This paper cites Bypassing the simulator: Near-optimal ad- versarial linear contextual bandits.Advances in Neural Information Processing Systems, 36: 52086–52131, 2023.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Bypassing the simulator: Near-optimal ad- versarial linear contextual bandits.Advances in Neural Information Processing Systems, 36: 52086–52131, 2023

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Observation 64d992e6-78aa-438e-8e85-dda65c4d6b09 · outbound

This paper cites Asymptotic properties of set membership identifi- cation algorithms.Systems & control letters, 27(3):145–155, 1996.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Asymptotic properties of set membership identifi- cation algorithms.Systems & control letters, 27(3):145–155, 1996

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Observation e7530efd-5776-4cc5-a4cf-f30070df1c5e · outbound

This paper cites Robust mpc with recursive model update.Automatica, 103:461–471, 2019.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Robust mpc with recursive model update.Automatica, 103:461–471, 2019

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Observation 902c725d-b57d-4d97-a22b-bdc97976494f · outbound

This paper cites Robust adaptive model predictive control: Performance and parameter estimation.International Journal of Robust and Nonlinear Control, 31(18):8703–8724, 2021.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Robust adaptive model predictive control: Performance and parameter estimation.International Journal of Robust and Nonlinear Control, 31(18):8703–8724, 2021

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Observation 023b732e-f5e5-42a3-a5c1-eedeb0767658 · outbound

This paper cites Optimistic bayesian sampling in contextual-bandit problems.Journal of Machine Learning Research, 13(6), 2012.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Optimistic bayesian sampling in contextual-bandit problems.Journal of Machine Learning Research, 13(6), 2012

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Observation d949158a-f532-4f32-b0f5-1e9c54c4be4a · outbound

This paper cites Optimal estimation theory for dynamic systems with set membership uncertainty: An overview.Automatica, 27(6):997–1009, 1991.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Optimal estimation theory for dynamic systems with set membership uncertainty: An overview.Automatica, 27(6):997–1009, 1991

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Observation 56882240-9c60-405e-b2db-8388231310c1 · outbound

This paper cites Identification of analytic nonlinear dynamical systems with non-asymptotic guarantees.Advances in Neural Information Processing Systems, 37:85500–85522, 2024.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Identification of analytic nonlinear dynamical systems with non-asymptotic guarantees.Advances in Neural Information Processing Systems, 37:85500–85522, 2024

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Observation d3b48bf5-e2e2-4bbb-87ec-abccd92b3e25 · outbound

This paper cites Efficient and robust algorithms for adversarial linear contextual bandits.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Efficient and robust algorithms for adversarial linear contextual bandits

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Observation a59b8272-d2cb-4b72-bafc-d158475efd54 · outbound

This paper cites Leveraging good representations in linear contextual bandits.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Leveraging good representations in linear contextual bandits

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Observation 47a2ea25-e4ba-4076-89e4-ae4735a4bbaf · outbound

This paper cites Contextual bandits and imitation learning with preference-based active queries.Advances in Neural Information Processing Systems, 36:11261–11295, 2023.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Contextual bandits and imitation learning with preference-based active queries.Advances in Neural Information Processing Systems, 36:11261–11295, 2023

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Observation d935f555-2876-49e4-a03b-0ecfc35aef90 · outbound

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

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Learning without mixing: Towards a sharp analysis of linear system identification

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Observation 3c8edfe2-3f14-48b0-bbf8-05da6748a50e · outbound

This paper cites Ensemble contextual bandits for personalized recommendation.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Ensemble contextual bandits for personalized recommendation

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Observation 9a67a18f-4415-4a98-bc58-648384a0c672 · outbound

This paper cites Stochastic linear contextual bandits with diverse contexts.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Stochastic linear contextual bandits with diverse contexts

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Observation fde446f1-8ad6-45e3-ab84-2e72e57ac582 · outbound

This paper cites On the sample complexity of set membership estimation for linear systems with disturbances bounded by convex sets.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach On the sample complexity of set membership estimation for linear systems with disturbances bounded by convex sets

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Observation 9c8965c2-eb64-420f-afc8-a4513d00b62a · outbound

This paper cites Noise-adaptive thompson sampling for linear contex- tual bandits.Advances in Neural Information Processing Systems, 36:23630–23657, 2023.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Noise-adaptive thompson sampling for linear contex- tual bandits.Advances in Neural Information Processing Systems, 36:23630–23657, 2023

Reference 56

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Observation b52afb4a-382e-435f-a402-025065a971b4 · outbound

This paper cites Dynamic clustering based contextual combinatorial multi-armed bandit for online recommendation.Knowledge- Based Systems, 257:109927, 2022.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Dynamic clustering based contextual combinatorial multi-armed bandit for online recommendation.Knowledge- Based Systems, 257:109927, 2022

Reference 57

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Observation 03e5d674-acb3-4fe0-8dc9-b9d0183305db · outbound

This paper cites Online adversarial stabilization of unknown net- worked systems.Proceedings of the ACM on Measurement and Analysis of Computing Systems, 7(1):1–43, 2023.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Online adversarial stabilization of unknown net- worked systems.Proceedings of the ACM on Measurement and Analysis of Computing Systems, 7(1):1–43, 2023

Reference 58

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Observation e0497ebd-093b-4ae3-97fe-76694de21583 · outbound

This paper cites System identification under bounded noise: Optimal rates beyond least squares.IEEE Control Systems Letters, 2025.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach System identification under bounded noise: Optimal rates beyond least squares.IEEE Control Systems Letters, 2025

Reference 59

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Observation b2fe9684-a338-4937-bcbf-80fd8f5b5014 · outbound

This paper cites Trajectory tracking control of autonomous ground vehicles using adaptive learning mpc.IEEE Transactions on Neural Networks and Learning Systems, 32(12):5554–5564, 2021.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Trajectory tracking control of autonomous ground vehicles using adaptive learning mpc.IEEE Transactions on Neural Networks and Learning Systems, 32(12):5554–5564, 2021

Reference 60

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Observation 5987564d-942b-4844-bbfd-866f232e5479 · outbound

This paper cites Feel-good thompson sampling for contextual bandits and reinforcement learning.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Feel-good thompson sampling for contextual bandits and reinforcement learning

Reference 61

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Observation a751b711-0970-4855-b9b3-983da9db1f07 · outbound

This paper cites Spoiled for choice? personalized recommendation for healthcare decisions: A multiarmed bandit approach.Information Systems Research, 34(4):1493–1512, 2023.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Spoiled for choice? personalized recommendation for healthcare decisions: A multiarmed bandit approach.Information Systems Research, 34(4):1493–1512, 2023

Reference 62

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Observation 415277b7-b9f8-4f9c-8c73-e117e0e691fb · outbound

This paper cites Thex t-weighted projection width ofE t−1 onto the vectorx t is Wt := 2 q x⊤ t Bt−1xt.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Thex t-weighted projection width ofE t−1 onto the vectorx t is Wt := 2 q x⊤ t Bt−1xt

Reference 63

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Observation fc192350-1ee8-4a07-b65a-0efe0434a43e · outbound

This paper cites Proof of Corollary 3.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Proof of Corollary 3

Reference 64

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Observation 8f4eecce-34bb-442b-8749-4ebc2e847973 · outbound

This paper cites Meanwhile, K µ 0 = [−2S,2S] d +µB d 2 ⊆B(0,2S √ d+µ).

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach Meanwhile, K µ 0 = [−2S,2S] d +µB d 2 ⊆B(0,2S √ d+µ)

Reference 65

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Observation cfe5f8e9-20f5-4c8d-92b6-17bd57bfabaf · outbound

This paper cites It follows that Φ0 −Φ T ≤dlog 2S √ d+µ µ !.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach It follows that Φ0 −Φ T ≤dlog 2S √ d+µ µ !

Reference 66

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Observation 15969e57-c510-45d8-9179-c97d784da461 · outbound

This paper cites E Numerical Settings Problem Settings.We utilize the experiment in [ 26] for our synthetic simulation settings.

Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach E Numerical Settings Problem Settings.We utilize the experiment in [ 26] for our synthetic simulation settings

Reference 67

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