Paper Citation Record · LEDGER

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance

As of 11 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 2 inbound Pith citation observations for arXiv:2502.06363.

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

pith.paper-citation-record.v1
2502.06363 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:48:55.889711Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:14:25.843225Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T04:14:28.393597Z

Reference resolution

34 of 34 outbound references displayed

verified exact2
verified fuzzy30
unresolved2
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f55205f9-67aa-49d3-b1dd-3ceb0bc4012a · outbound

This paper cites Online learning for linearly parametrized control problems.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Online learning for linearly parametrized control problems

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-08T15:48:55.747350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98478988-f2a4-46b0-866a-ae2d502db6e3 · outbound

This paper cites Schoellig.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Schoellig

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.375232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b887ce13-39bc-47f3-9955-24ee7643bfc7 · outbound

This paper cites Convergence rates of efficient global optimization algorithms.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Convergence rates of efficient global optimization algorithms

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.362708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ce0b2e55-0493-40f8-b2f7-fa5d855dd043 · outbound

This paper cites On lower bounds for standard and robust G aussian process bandit optimization.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance On lower bounds for standard and robust G aussian process bandit optimization

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.350202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5cb2c9f3-45a8-480c-8f66-84e1420869d0 · outbound

This paper cites High-dimensional experimental design and kernel bandits.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance High-dimensional experimental design and kernel bandits

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.337677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cac1ac71-263c-4b16-8439-75202a9f46a5 · outbound

This paper cites On kernelized multi-armed bandits.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance On kernelized multi-armed bandits

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.323553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6a0d1f94-8034-4088-b961-09e190e0a56d · outbound

This paper cites Cover and Joy A.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Cover and Joy A

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.309504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 98d2125b-aef9-4a50-a74f-5c40aa6490c4 · outbound

This paper cites Smola, and Masrour Zoghi.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Smola, and Masrour Zoghi

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.295192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 44664c0f-a0b1-448f-8ced-385c54dfbbbf · outbound

This paper cites Rapid, accurate, and precise concentration measurements of a methanol--water mixture using R aman spectroscopy.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Rapid, accurate, and precise concentration measurements of a methanol--water mixture using R aman spectroscopy

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.280951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c50338e9-8031-4be9-9761-bb5ca4f86419 · outbound

This paper cites Bandit optimisation of functions in the M at^^c3^^a9rn kernel RKHS.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Bandit optimisation of functions in the M at^^c3^^a9rn kernel RKHS

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.266531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8b4b6ad4-095f-426f-a332-dec9d7d766db · outbound

This paper cites Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-08T15:48:55.950249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4f77c6ef-2606-4e1d-89ee-1db69e6dc233 · outbound

This paper cites Improved regret analysis for variance-adaptive linear bandits and horizon-free linear mixture MDP s.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Improved regret analysis for variance-adaptive linear bandits and horizon-free linear mixture MDP s

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.250841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6eedc267-b978-412c-bbc7-103c1fd66797 · outbound

This paper cites Information directed sampling and bandits with heteroscedastic noise.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Information directed sampling and bandits with heteroscedastic noise

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.233740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dee26dff-409c-4feb-8e95-05ba44484e4f · outbound

This paper cites Multi-scale zero-order optimization of smooth functions in an RKHS.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Multi-scale zero-order optimization of smooth functions in an RKHS

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.219353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0144b295-bedf-4536-9286-917f85a7b655 · outbound

This paper cites B ayesian optimization with adaptive surrogate models for automated experimental design.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance B ayesian optimization with adaptive surrogate models for automated experimental design

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.204694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 92cc1fe9-4e52-48b8-9568-09104a10db3b · outbound

This paper cites G aussian process bandit optimization with few batches.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance G aussian process bandit optimization with few batches

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.190712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 75245271-cc95-457c-bca4-052accccd658 · outbound

This paper cites Efficient Batch Black-box Optimization with Deterministic Regret Bounds.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Efficient Batch Black-box Optimization with Deterministic Regret Bounds

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-08T15:48:55.931319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ca1f3692-1728-4452-a8fa-c1a79c4da812 · outbound

This paper cites Risk-averse heteroscedastic B ayesian optimization.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Risk-averse heteroscedastic B ayesian optimization

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.177489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bbdbbde2-f77f-4c1a-a209-ecda88dfc0df · outbound

This paper cites Learning to optimize via posterior sampling.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Learning to optimize via posterior sampling

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T15:48:55.828398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f806d15-49ae-48ca-b8fc-53e36eba05b1 · outbound

This paper cites A domain-shrinking based B ayesian optimization algorithm with order-optimal regret performance.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance A domain-shrinking based B ayesian optimization algorithm with order-optimal regret performance

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.154641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a88ac371-7c60-45ce-b3d2-a62f0a4494fe · outbound

This paper cites Random exploration in B ayesian optimization: O rder-optimal regret and computational efficiency.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Random exploration in B ayesian optimization: O rder-optimal regret and computational efficiency

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.141938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c0b5df4c-99fe-41be-9b27-7ab4f617bf2d · outbound

This paper cites Tight regret bounds for B ayesian optimization in one dimension.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Tight regret bounds for B ayesian optimization in one dimension

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.129529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 01818107-8b45-45c1-aa5d-8b3c16bd745b · outbound

This paper cites Lower bounds on regret for noisy G aussian process bandit optimization.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Lower bounds on regret for noisy G aussian process bandit optimization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.116025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dd4c6b1d-2d9f-4b3a-b2bb-3b481d4789c4 · outbound

This paper cites Practical B ayesian optimization of machine learning algorithms.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Practical B ayesian optimization of machine learning algorithms

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.102444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a09387a0-c582-4ebf-a714-d29967028637 · outbound

This paper cites Gaussian process optimization in the bandit setting: No regret and experimental design.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Gaussian process optimization in the bandit setting: No regret and experimental design

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.088888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation be5ca987-9371-4787-b686-23d1bbd15e90 · outbound

This paper cites Randomized G aussian process upper confidence bound with tighter B ayesian regret bounds.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Randomized G aussian process upper confidence bound with tighter B ayesian regret bounds

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.074423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 958a48f3-27cc-448b-a0d2-f8d699aab517 · outbound

This paper cites Posterior sampling-based B ayesian optimization with tighter B ayesian regret bounds.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Posterior sampling-based B ayesian optimization with tighter B ayesian regret bounds

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.060730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 21645891-f09a-4df9-9e63-9c42ed7f8b94 · outbound

This paper cites Open problem: R egret bounds for noise-free kernel-based bandits.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Open problem: R egret bounds for noise-free kernel-based bandits

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.046988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3ebb5d2e-d6dd-4e89-8e44-f3a252daa0fc · outbound

This paper cites Optimal order simple regret for G aussian process bandits.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Optimal order simple regret for G aussian process bandits

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.033065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c1632b7d-03d0-4c28-bf44-b96d16aff292 · outbound

This paper cites Finite-time analysis of kernelised contextual bandits.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Finite-time analysis of kernelised contextual bandits

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.018982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0f0c1f46-9769-41f1-9a99-71577b01ebc3 · outbound

This paper cites Improved variance-aware confidence sets for linear bandits and linear mixture MDP.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Improved variance-aware confidence sets for linear bandits and linear mixture MDP

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:56.005228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 53a30090-8fd2-463f-8301-cd3798c1956a · outbound

This paper cites Variance-dependent regret bounds for linear bandits and reinforcement learning: A daptivity and computational efficiency.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Variance-dependent regret bounds for linear bandits and reinforcement learning: A daptivity and computational efficiency

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:55.991316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 261e64f5-eea9-49cd-8805-cdf5865b358f · outbound

This paper cites Computationally efficient horizon-free reinforcement learning for linear mixture MDP s.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Computationally efficient horizon-free reinforcement learning for linear mixture MDP s

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:55.977845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 670e79bb-7542-4059-9e9a-c18c0a7514cc · outbound

This paper cites Nearly minimax optimal reinforcement learning for linear mixture M arkov decision processes.

Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance Nearly minimax optimal reinforcement learning for linear mixture M arkov decision processes

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:48:55.963874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation 88e4ba9e-9f4f-4530-90e1-c179bc306919 · inbound

Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient? cites this paper.

Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient? Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:14:28.448516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 29c7a18c-7fb9-4753-a238-63a18cf305c8 · inbound

No-Regret Gaussian Process Optimization of Time-Varying Functions cites this paper.

No-Regret Gaussian Process Optimization of Time-Varying Functions Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T19:33:06.897998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.