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Paper Citation Record · LEDGER

Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1901.00301.

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

pith.paper-citation-record.v1
1901.00301 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:54:46.799163Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-23T22:33:32.364287Z

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 9afe92f9-c7cb-48f2-af42-c402afa925cc · inbound

Identifiable Latent Bandits: Leveraging observational data for personalized decision-making cites this paper.

Identifiable Latent Bandits: Leveraging observational data for personalized decision-making Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

Reference 55

Resolution
verified exact
local_arxiv, observed 2026-05-23T22:33:32.367338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-23T22:33:23.253118Z digest=sha256:8feaaf15992cebb90ff90626615aac352ba82d2ec1ffb20828872cecb32a28d9

Observation f15378e9-653e-42c4-ad62-4a497a81411a · inbound

Deconfounded Warm-Start Thompson Sampling with Applications to Precision Medicine cites this paper.

Deconfounded Warm-Start Thompson Sampling with Applications to Precision Medicine Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-07T14:54:46.799163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:54:46.799163Z digest=sha256:98665f491c58732ac7ebb4ef14e7cff01376402511bb0136edb6204d2b86f222

Observation 54377027-a219-48c0-b696-6c15fabe0883 · inbound

Best Arm Identification with Possibly Biased Offline Data cites this paper.

Best Arm Identification with Possibly Biased Offline Data Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T13:07:13.128104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:07:13.128104Z digest=sha256:fd810c01a894a4acb52cdc0354f3d1b31bd7c476de43c77dd915f4aa8b188f1a

Observation d24b2dc7-e180-48a6-9397-e42223ecda72 · inbound

Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards cites this paper.

Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-05-19T08:52:13.674525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T08:50:13.396492Z digest=sha256:8961218d0e80b0641851a4e3f095c730fcd02ad2327f75c6229ccfafbf73d782

Observation e2f5db4e-ce7f-4cb1-8233-c99ba55cd10e · inbound

Contextual Online Pricing with (Biased) Offline Data cites this paper.

Contextual Online Pricing with (Biased) Offline Data Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-06T20:33:16.976339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:33:16.976339Z digest=sha256:c354fdad1c5dbde8ab14d838e6b6b6d327ba67e60eb9fa3cb0d519824f757d1a