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Paper Citation Record · LEDGER

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

As of 18 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 3 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 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:26:20.064853Z

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T00:44:27.481704Z digest=sha256:c326b646fe41f848adb7e9c437f6b9c41033cc1f6444c5f41b886cadb994a247

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T00:44:26.609784Z digest=sha256:1ac2d9330678a6ac14b24c42acfe4b54adf2778e25a951b0776eb258365aabce

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=sha256:573655aa9ea55b1fb7b0e643265a39c07c9f347152a352d1d4dfee391af399e8

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T00:44:27.569160Z digest=sha256:08e5b5d2213cf4f7043b298cf26437cc37cdb51334460bd230877bb8c2eea81e

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T00:44:27.366740Z digest=sha256:a2bba0c0a2eb3fe8528d6b4ee3a5e0cae890f3ed9f40cbf87582d7f739860ba4

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T00:44:26.846239Z digest=sha256:683b6c917a8d948ec9217ad458925074a12543f78fdbeeabee2d9472db65506a

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T00:44:25.993914Z digest=sha256:c1ec14d03c12e49cf039602d0fd0ff10c9c2a2d548ed2d170896d4b9043e364c

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=sha256:9265210a1826c30af2452557356699b345f9991c205ac4df235d66bcd6cad282

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:c3dfdad2ba881439f90920bb9a98659e2000a22aac2c4c085328db2655cba500

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=sha256:722f5202b9ee9717f7fd2c39e2d47cc7c211415bbfaccfe029f4702040f8003b

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=sha256:4033b761a9ebbcafe9812979aa403c7ac6382ef07a221eb59038409a54a891d9

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T00:44:26.277453Z digest=sha256:8e944472a24cc522c530992e7c23490d170ebe3ba6072101c6efb56cb5561573

Pith citing papers

Observation 77dc10f0-8bf7-4402-b814-b7b026f750d6 · inbound

Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints cites this paper.

Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need!

Reference 2013

Resolution
unresolved
no resolver link, observed 2026-08-15T17:26:20.064853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:26:20.064853Z digest=sha256:cc4f965dbba5973388d32e346c8682e43f567c596e874951985125825c6c5dea

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-08T13:36:34.355048Z digest=sha256:bf9687c7fd205fe6941d7b85ba207e080e8e5175b5f34c4fb238d16bd7798d47

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 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-21T06:23:20.784755Z digest=sha256:c8b411aeb38d9eebb8d4ef0783492f379da94f4c390937123975485fdb11a4d0