Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T17:26:20.109783Z
Paper Citation Record · LEDGER
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints
As of 19 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2508.16992.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
15 of 15 outbound references displayed
- verified exact1
- verified fuzzy8
- unresolved6
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 9ad2c0e9-3b39-4d15-a39b-adec46a1f266 · outbound
This paper cites We now modify the policy π to obtain a new online policy π′ which is feasible.
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints We now modify the policy π to obtain a new online policy π′ which is feasible
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:26:20.288578Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 0c6ab411-1caf-445c-b14a-7128e73cc7a1 · outbound
This paper cites In phase σ∈ [τ], armA1 has reward σBT/T in each round.
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints In phase σ∈ [τ], armA1 has reward σBT/T in each round
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:26:20.303614Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9f03c7ee-b0f8-4f32-aa26-384896d97064 · outbound
This paper cites 7: Estimation Scheme: ˜ℓt(i) = ˆℓt(it) p′ t(it)1(it =i),∀i.
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints 7: Estimation Scheme: ˜ℓt(i) = ˆℓt(it) p′ t(it)1(it =i),∀i
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:26:20.239776Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 06b96523-f199-430c-981d-902da0960499 · outbound
This paper cites A.2 Proof of Theorem 3 (1) =⇒ (2): Since f is α−approximately convex, for a given x∈X , ∃g′∈ Rn s.t.
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints A.2 Proof of Theorem 3 (1) =⇒ (2): Since f is α−approximately convex, for a given x∈X , ∃g′∈ Rn s.t
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:26:20.367205Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 24ae2e49-1f62-4754-97e7-025909e7efd4 · outbound
This paper cites Then from Theorem 3, part 3, there exists a convex function g such that g(x)≤ f(x)≤ αg(x),∀x∈X.
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints Then from Theorem 3, part 3, there exists a convex function g such that g(x)≤ f(x)≤ αg(x),∀x∈X
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:26:20.337162Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation d9f5ebe2-40b5-49df-8c61-f91de47bf054 · outbound
This paper cites an unresolved cited work.
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints Unresolved cited work
Reference 9
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T17:26:20.321412Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 51f8fbb1-c1db-4f8a-9116-d4d328b6bdb6 · outbound
This paper cites Ea⋆∼D TX t=1 ct(a⋆)≤BT.
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints Ea⋆∼D TX t=1 ct(a⋆)≤BT
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:26:20.256324Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a4178b56-8c23-488d-93b5-4cb7fbc8b09e · outbound
This paper cites The setting we consider here is the same as the Bandits with Knapsacks (BwK) problem, considered by Immorlica et al.
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints The setting we consider here is the same as the Bandits with Knapsacks (BwK) problem, considered by Immorlica et al
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:26:20.272540Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 0deb923f-17d6-4b7b-bca2-3358153a92ad · outbound
This paper cites Online learning with knapsacks: the best of both worlds.
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints Online learning with knapsacks: the best of both worlds
Reference 2004
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T17:26:20.381555Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 77dc10f0-8bf7-4402-b814-b7b026f750d6 · outbound
This paper cites No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need!.
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.
Observation 435b6d40-9bfe-4cf5-94f7-7134c719e9b0 · outbound
This paper cites Online Learning: A Modern Introduction Using Convex Optimization.
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints Online Learning: A Modern Introduction Using Convex Optimization
Reference 2017
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:26:20.047505Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 546a96fc-f3d1-4eb3-a4d0-a3e54b1b8fe5 · outbound
This paper cites an unresolved cited work.
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints Unresolved cited work
Reference 2022
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T17:26:20.351794Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 20d2a569-2603-4e60-ae19-daeaa9410a0f · outbound
This paper cites Online Convex Optimization with Time-Varying Constraints.
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints Online Convex Optimization with Time-Varying Constraints
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:26:20.042013Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a3c323dd-e013-4e1a-a094-9825d1e5a55d · outbound
This paper cites Dynamic Ad Allocation: Bandits with Budgets.
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints Dynamic Ad Allocation: Bandits with Budgets
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:26:20.059488Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7ee1a365-e788-4a09-882c-b3120bdee491 · outbound
This paper cites BanditQ: Fair Bandits with Guaranteed Rewards.
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints BanditQ: Fair Bandits with Guaranteed Rewards
Reference 2025
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T17:26:20.186179Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Pith citing papers
No inbound Pith citation observations are available.