Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
KL-UCB-switch: optimal regret bounds for stochastic bandits from both a distribution-dependent and a distribution-free viewpoints
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1805.05071.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T12:58:10.434294Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T12:26:08.922944Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 35c38755-4f24-43ff-8b16-d53c0997f5dc · inbound
Accelerated learning from recommender systems using multi-armed bandit cites this paper.
Accelerated learning from recommender systems using multi-armed bandit KL-UCB-switch: optimal regret bounds for stochastic bandits from both a distribution-dependent and a distribution-free viewpoints
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:58:10.434294Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a60d3712-b311-4d9c-96db-63692e172048 · inbound
Budgeted Online Influence Maximization cites this paper.
Budgeted Online Influence Maximization KL-UCB-switch: optimal regret bounds for stochastic bandits from both a distribution-dependent and a distribution-free viewpoints
Reference 74
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T12:26:08.926507Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation bcdc0c60-f423-4af0-9e75-bb72101a73fc · inbound
Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification cites this paper.
Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification KL-UCB-switch: optimal regret bounds for stochastic bandits from both a distribution-dependent and a distribution-free viewpoints
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T09:55:52.708972Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.