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Paper Citation Record · LEDGER

Contextual Learning for Stochastic Optimization

As of 20 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2505.16829.

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

pith.paper-citation-record.v1
2505.16829 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:04:08.460029Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

  • verified exact2
  • verified fuzzy24
  • unresolved4
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ba893858-a2f1-4747-a77f-500942ab4e20 · outbound

This paper cites Taming the monster: A fast and simple algorithm for contextual bandits.

Contextual Learning for Stochastic Optimization Taming the monster: A fast and simple algorithm for contextual bandits

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:12.707621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:06.224888Z digest=sha256:200307e8cba4cff9941337610dcdf444493c7cb80859ba119cb08e556f64aadf

Observation 71c1f5ea-7802-4d9b-98f9-d2b03e3f7fe4 · outbound

This paper cites Semi-bandit learning for monotone stochastic optimization.

Contextual Learning for Stochastic Optimization Semi-bandit learning for monotone stochastic optimization

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:12.653802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:06.306072Z digest=sha256:9ce784a167c63bd0eda219696cb7df5ff2dd1c1feae526d4c25fe5fc57a3b8d9

Observation e2419198-beb3-4760-90cc-2e4e573743c1 · outbound

This paper cites Contextual pandora’s box.

Contextual Learning for Stochastic Optimization Contextual pandora’s box

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:12.584696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:06.390919Z digest=sha256:628e3fd5ddb0d78274f484964046c35c53b177f4ae1ddeb136cf82692f7cfcfc

Observation 24f0dcb3-0857-4003-baf1-8a7b0918e39d · outbound

This paper cites Multi-item mechanisms without item-independence: Learnability via robustness.

Contextual Learning for Stochastic Optimization Multi-item mechanisms without item-independence: Learnability via robustness

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:12.493232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:06.493541Z digest=sha256:e90faa50544f7eaed92a445b02c7a6096eee2351362bbe48f0051ec149228024

Observation 66541f08-2550-45a4-9ad0-3f7eb20c6607 · outbound

This paper cites Nonparametric pricing analytics with customer covariates.

Contextual Learning for Stochastic Optimization Nonparametric pricing analytics with customer covariates

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:12.416443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:06.580969Z digest=sha256:111ebe14c489cc5a27610ad0e0afdd381071ee800bd641ab98bf6dd379764870

Observation 604b8b84-ba73-4a04-808b-4ebd76d51a71 · outbound

This paper cites Contextual bandits with linear payoff functions.

Contextual Learning for Stochastic Optimization Contextual bandits with linear payoff functions

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:12.342906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:06.680263Z digest=sha256:72163914e9dc07f41116e26cecdf34fab4516ba080f94606ebf1c214bb97bea6

Observation 7b0a4a27-619a-4d64-be38-83f1d7404f5d · outbound

This paper cites Cohen, Ilan Lobel, and Renato Paes Leme.

Contextual Learning for Stochastic Optimization Cohen, Ilan Lobel, and Renato Paes Leme

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:12.209175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:06.766503Z digest=sha256:a1aeae06b3138e4cebd212c249dd6e5513b6d628104440c6a60fbdafa9478e78

Observation 17eb02f9-81a3-4ce5-8356-84275b67272d · outbound

This paper cites The sample complexity of revenue maximization.

Contextual Learning for Stochastic Optimization The sample complexity of revenue maximization

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:12.145841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:06.848385Z digest=sha256:b2fe364ff2afddf3787e5352775dd840975b12ac4ffb1b701a6c33601d581311

Observation bfccfe50-36f5-4462-a6b2-8b0680ea850c · outbound

This paper cites Prophet inequalities for independent and identically distributed random variables from an unknown distribution.

Contextual Learning for Stochastic Optimization Prophet inequalities for independent and identically distributed random variables from an unknown distribution

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:11.971477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:06.937983Z digest=sha256:98128a5e118e76d1e736b3cfbedb86e08fc7224513cd4d02276812f002f17e31

Observation 64fc9806-31a8-4ce5-a061-5d996a1f37a5 · outbound

This paper cites The sample complexity of auctions with side information.

Contextual Learning for Stochastic Optimization The sample complexity of auctions with side information

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:11.818382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:06.990880Z digest=sha256:8020c3122a9741e2a77112992f6b53c320915ea3961328b7090d9cc924d0066e

Observation 9fa99486-84ec-4408-a5d4-dc6c9e666b29 · outbound

This paper cites Efficient optimal leanring for contextual bandits.

Contextual Learning for Stochastic Optimization Efficient optimal leanring for contextual bandits

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:11.706089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:07.060155Z digest=sha256:a7c079e8ca884452f979108a964c2977bb59c36f532033210eb2648b50349db7

Observation 8474fd59-2328-409e-8d4c-5390c1aa3b7d · outbound

This paper cites Posted pricing and prophet inequalities with inaccurate priors.

Contextual Learning for Stochastic Optimization Posted pricing and prophet inequalities with inaccurate priors

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:11.572431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:07.164398Z digest=sha256:7d26bc2dc867e14b497fadc2198e61d8f6b3f9c4579eeee465409ee6f6a27bc2

Observation 69fe6b44-ca47-437d-ac6b-4f092eb7d2ec · outbound

This paper cites Practical contextual bandits with regression oracles.

Contextual Learning for Stochastic Optimization Practical contextual bandits with regression oracles

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:11.399287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:07.262706Z digest=sha256:0c63c04d1d081859588292a546367932e14b8d09475acb05b57facab2282f8cf

Observation cf379dee-b170-463c-9614-64c1e8ea623a · outbound

This paper cites Bandit algorithms for prophet inequality and pandora's box.

Contextual Learning for Stochastic Optimization Bandit algorithms for prophet inequality and pandora's box

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:11.281715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:07.319928Z digest=sha256:e29dfa81da8b3924d26bf09ad3548598650b8e19d00326ef0226983e6f724890

Observation f8d2e820-fad7-4d0b-85d8-771c1be56d4b · outbound

This paper cites Online learning for min sum set cover and pandora’s box.

Contextual Learning for Stochastic Optimization Online learning for min sum set cover and pandora’s box

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:11.168464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:07.378413Z digest=sha256:ccc1e1800831c35f4aa5cdabe41247d63cf953cb6fa868e4ca2df77fdf572bf5

Observation 319665ef-1851-47cd-958b-4ccb6118b0de · outbound

This paper cites On choosing and bounding probability metrics.

Contextual Learning for Stochastic Optimization On choosing and bounding probability metrics

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T15:04:07.438125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:04:07.438125Z digest=sha256:3054d9a6b275236b40f8ac416e6580e5af5ef12d4fc86cafed0ea8cc5f0b92b1

Observation f63aabd0-1449-4f0b-9905-28c793af223a · outbound

This paper cites Generalizing complex hypotheses on product distributions: Auctions, prophet inequalities, and pandora’s problem.

Contextual Learning for Stochastic Optimization Generalizing complex hypotheses on product distributions: Auctions, prophet inequalities, and pandora’s problem

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:11.014023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:07.499022Z digest=sha256:3e764e1b3a2519ac55292c237822efcfce22e444f64f251804e1203e3624891e

Observation 2bc15d16-af47-4c35-bbbb-fa5c35e4a0b1 · outbound

This paper cites Probability inequalities for sums of bounded random variables.

Contextual Learning for Stochastic Optimization Probability inequalities for sums of bounded random variables

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T15:04:07.599840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:04:07.599840Z digest=sha256:aef68d14e67149815c42e87675bbba54f2b5568ff3398dda50f591f7d06e1122

Observation 117bf9fd-d77b-4f41-bc42-4bb527d169bc · outbound

This paper cites Dynamic pricing in high-dimensions.

Contextual Learning for Stochastic Optimization Dynamic pricing in high-dimensions

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:10.837060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:07.673515Z digest=sha256:41a2a17c882fcbf430774b5a33462bf6dd380bfe1ec14e6deb7ac9a7d165e46b

Observation 0a0be16e-240e-4a24-98e9-57052a3b918b · outbound

This paper cites Sample complexity of posted pricing for a single item.

Contextual Learning for Stochastic Optimization Sample complexity of posted pricing for a single item

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:10.759602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:07.752507Z digest=sha256:d48a544ecaab65c8b53f138024b7f915080060ee43ea1b13f0273194375d3c5c

Observation fa404ec8-d619-4a7b-8810-bab4b15d4fa4 · outbound

This paper cites The epoch-greedy algorithm for multi-armed bandits with side information.

Contextual Learning for Stochastic Optimization The epoch-greedy algorithm for multi-armed bandits with side information

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:10.618895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:07.846511Z digest=sha256:5fc2d63644ab98eb8880a426a99dbbd193d5fecc424a47af7c678c0ef5000e57

Observation 93b849b3-bf1b-44ae-9ad1-be32fadd313c · outbound

This paper cites Distribution-free contextual dynamic pricing.

Contextual Learning for Stochastic Optimization Distribution-free contextual dynamic pricing

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:10.327162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:07.921986Z digest=sha256:a349cc530246e846dfdfc36605576b9c67888899cb9c691c5330c6408878e315

Observation 7e327a61-244f-4dfa-a6b3-0bb90f27c72b · outbound

This paper cites Ironing in the Dark.

Contextual Learning for Stochastic Optimization Ironing in the Dark

Reference 23

Resolution
verified exact
local_arxiv, observed 2026-08-07T15:04:09.008170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:07.993104Z digest=sha256:d319efc41d0176199f017ba36dd3de55ac6bf11d33d5ab138a544c3021e92366

Observation 4521303f-b6db-4482-aebc-18b35cd93ca7 · outbound

This paper cites Learnability, stability and uniform convergence.

Contextual Learning for Stochastic Optimization Learnability, stability and uniform convergence

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:10.035376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:08.096549Z digest=sha256:9314eeb81a273695826dfca96e38dc7ee4b0cce1316177777ec38157fd45807c

Observation 32fdd793-ffee-45f7-ba4d-648d7379fa94 · outbound

This paper cites Bypassing the monster: A faster and simpler optimal algorithm for contextual bandits under realizability.

Contextual Learning for Stochastic Optimization Bypassing the monster: A faster and simpler optimal algorithm for contextual bandits under realizability

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-07T15:04:08.162193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:04:08.162193Z digest=sha256:24b37380395a82f88672c4a90ef40637a16b9b93493c5b11b36f117581626b62

Observation b09ef69b-bb59-48c1-afec-ab0a4b48cd47 · outbound

This paper cites Contextual bandits with packing and covering constraints: A modular lagrangian approach via regression.

Contextual Learning for Stochastic Optimization Contextual bandits with packing and covering constraints: A modular lagrangian approach via regression

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:09.781344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:08.235208Z digest=sha256:73cbd8dde6ae5cc0bfa157d8b70e3edba42ea4efa9a53d30039b34a75596d8ef

Observation 8af057ab-f41c-41a6-8f90-8da824c29c4e · outbound

This paper cites Improved Algorithms for Contextual Dynamic Pricing.

Contextual Learning for Stochastic Optimization Improved Algorithms for Contextual Dynamic Pricing

Reference 27

Resolution
verified exact
local_arxiv, observed 2026-08-07T15:04:08.756614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:08.331599Z digest=sha256:67ecf7bcdf86a8e1b9941297fa76a78ee6730198453566c9c37949bec3e47ae9

Observation ca0f23e9-5f65-4828-a957-d45b030f12bb · outbound

This paper cites The nature of statistical learning theory.

Contextual Learning for Stochastic Optimization The nature of statistical learning theory

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:09.527489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:08.403587Z digest=sha256:6f7b1fa3156013e4af3f457b711cbbbb13b8d5fa7a17e597fcd8d1222ad32c60

Observation 8668bfa3-5bbe-40d4-805f-605e7e78421c · outbound

This paper cites Weitzman.

Contextual Learning for Stochastic Optimization Weitzman

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T15:04:08.453247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:04:08.453247Z digest=sha256:bb971bad9de3c41230ce0d86242922e40612f7367227fed6cf0e83b99cdb27be

Observation a42e832f-fc90-405a-a34c-661d816b832d · outbound

This paper cites Towards agnostic feature-based dynamic pricing: Linear policies vs linear valuation with unknown noise.

Contextual Learning for Stochastic Optimization Towards agnostic feature-based dynamic pricing: Linear policies vs linear valuation with unknown noise

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:04:09.265381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T15:04:08.460029Z digest=sha256:8db1a21b7a8c7b1f555fab72dc96d155597d1e7b7375fe3a0fbdf6a4643d4a7c

Pith citing papers

No inbound Pith citation observations are available.