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Paper Citation Record · LEDGER

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need!

As of 10 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 2 inbound Pith citation observations for arXiv:2506.13244.

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

pith.paper-citation-record.v1
2506.13244 v3

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:44:27.569160Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T06:23:20.784755Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T06:24:00.495250Z

Reference resolution

12 of 12 outbound references displayed

  • verified exact1
  • verified fuzzy5
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 749bf43f-291f-408b-b111-8593be55fd5d · outbound

This paper cites [2023a] focus on online allocation with a single resource.

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need! [2023a] focus on online allocation with a single resource

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:28.868007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T00:44:27.481704Z digest=sha256:59d2ce3b1d49c24beb565af223f18d2cede044e0dc9aaf718228044872180964

Observation 724166f9-8385-4d40-96d4-58fdd462ea0e · outbound

This paper cites Adversarial bandits with knapsacks.

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need! Adversarial bandits with knapsacks

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:29.498765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T00:44:26.609784Z digest=sha256:0c9b87be1b902304e32ba505c0538109132dfbf49647e61c0c549aee00eb0cfc

Observation 682e3d12-d6a2-40af-a2d5-2fd95bf9efa7 · outbound

This paper cites Online Stochastic Optimization with Wasserstein Based Non-stationarity.

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need! Online Stochastic Optimization with Wasserstein Based Non-stationarity

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T00:44:26.727369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:44:26.727369Z digest=sha256:0d3a472ea450399587176bed356ee4f368544b340f4eb58b46905f4c0e831be8

Observation 3e2019fd-c2a0-45a5-b72e-d8bf4ee1bd98 · outbound

This paper cites Learning with General ConstraintsThere exists an extended literature on online learning problem with general constraints (e.g., [Mannor et al., 2009, Liakopoulos et al., 2019]).

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need! Learning with General ConstraintsThere exists an extended literature on online learning problem with general constraints (e.g., [Mannor et al., 2009, Liakopoulos et al., 2019])

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:28.563399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T00:44:27.569160Z digest=sha256:6871dbe5d3b57e0baffcf8cbc9331acec5e26e5d1d6b9ce9680902e26292903a

Observation c3517586-4ea4-46a6-af47-4b099fe68fcf · outbound

This paper cites Later, Agrawal et al.

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need! Later, Agrawal et al

Reference 2010

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:29.112056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T00:44:27.366740Z digest=sha256:de64d7d4556ccd7e7706416ebb83ceb188b27698e5648dd7f924ef8995b60f7c

Observation 2fd1b0ae-2a62-400a-a190-de0b67619e07 · outbound

This paper cites Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets.

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need! Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets

Reference 2014

Resolution
verified exact
local_arxiv, observed 2026-08-07T00:44:27.892664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T00:44:26.846239Z digest=sha256:8b3239f11a3d6087f52245b02bfc18f75d9f6ba83e03dea73abd396761479349

Observation 56dd4fff-747a-4e23-98de-7158cb074772 · outbound

This paper cites Bandits with knapsacks.

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need! Bandits with knapsacks

Reference 2016

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:44:29.842921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T00:44:25.993914Z digest=sha256:d94c018e692a060016375aa094b0e0cb2212e7d74b9f8f386562c3b3b6d7ebc5

Observation 8c408b1d-45ae-4f0e-be9a-c40a6b2ffdb9 · outbound

This paper cites Provably Efficient Model-Free Algorithm for MDPs with Peak Constraints.

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need! Provably Efficient Model-Free Algorithm for MDPs with Peak Constraints

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-07T00:44:26.151610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:44:26.151610Z digest=sha256:061ab7c791ec26b485e64cc759901dbbf8193850e841b8f822c0958069e784c0

Observation f0b63ff1-abc4-4b06-9e7e-4acfa4241994 · outbound

This paper cites Online Learning: A Modern Introduction Using Convex Optimization.

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need! Online Learning: A Modern Introduction Using Convex Optimization

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-07T00:44:27.137290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:44:27.137290Z digest=sha256:de46446529b963475690f8ca77394fb2796865e5595262eef3045ba001560f01

Observation e0820b41-f4b5-402f-aef3-21ef84b9141e · outbound

This paper cites Exploration-Exploitation in Constrained MDPs.

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need! Exploration-Exploitation in Constrained MDPs

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-07T00:44:26.431839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:44:26.431839Z digest=sha256:b16368a02f61a8c60b4ec79b0a89b2d562415d06e471208a917f1f3359421360

Observation a48b5e45-c020-4f85-983c-bea6457fb9b8 · outbound

This paper cites Learning Adversarial MDPs with Stochastic Hard Constraints.

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need! Learning Adversarial MDPs with Stochastic Hard Constraints

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T00:44:27.212457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:44:27.212457Z digest=sha256:51fe253f8a3fb91d13f0b6fecaddca763c4e148730716faac1f194eb117e0440

Observation b34d673c-8cec-4b41-ac58-f0601f8f7796 · outbound

This paper cites A New Benchmark for Online Learning with Budget-Balancing Constraints.

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need! A New Benchmark for Online Learning with Budget-Balancing Constraints

Reference 2025

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T00:44:28.154591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-07T00:44:26.277453Z digest=sha256:72af38489e7340f42286635bff0f8c6d7676235ce11fecfc821731cb25db4329

Pith citing papers

Observation c8c5cc38-7aec-449f-84e3-ae727dba3dab · inbound

Constrained Contextual Bandits with Adversarial Contexts cites this paper.

Constrained Contextual Bandits with Adversarial Contexts No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need!

Reference 267

Resolution
verified exact
arxiv_id, observed 2026-05-11T18:51:08.094234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-08T13:36:34.355048Z digest=sha256:f30d1ddf1f62f59c177d99c2c492cd176bc12bc6e21af7dc93cfb6feb98392c6

Observation 5c4b23db-c002-43cc-b389-2161da616cb5 · inbound

A Geometric Approach to Constrained Online Learning cites this paper.

A Geometric Approach to Constrained Online Learning No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need!

Reference 277

Resolution
verified exact
arxiv_id, observed 2026-05-21T06:24:00.496839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-21T06:23:20.784755Z digest=sha256:ca6a2951c76d11438cf80e3ebe0680011c2bac2f213d36284730a590946c1b5a