Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:19:55.119600Z
Paper Citation Record · LEDGER
How hard is learning to cut? Trade-offs and sample complexity
As of 18 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 1 inbound Pith citation observation for arXiv:2506.00252.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-11T02:10:44.402412Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T03:50:58.014773Z
Reference resolution
22 of 22 outbound references displayed
- verified exact1
- verified fuzzy17
- unresolved3
- parse uncertain1
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation d1d66e27-b2e9-413a-b213-cfa40ec71060 · outbound
This paper cites Neural network learning: Theoretical foundations.
How hard is learning to cut? Trade-offs and sample complexity Neural network learning: Theoretical foundations
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:58.719610Z
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.
Observation c026e06a-66c2-442a-9cd0-e947b6c56254 · outbound
This paper cites Nearly-tight vc-dimension and pseudodimension bounds for piecewise linear neural networks.
How hard is learning to cut? Trade-offs and sample complexity Nearly-tight vc-dimension and pseudodimension bounds for piecewise linear neural networks
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:58.555648Z
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.
Observation de262358-c797-4338-ba5f-47ed0abd96f1 · outbound
This paper cites Sample complexity of tree search configuration: Cutting planes and beyond.
How hard is learning to cut? Trade-offs and sample complexity Sample complexity of tree search configuration: Cutting planes and beyond
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:58.343515Z
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.
Observation cf30c805-1729-408e-a2ab-7a589fe5a5b5 · outbound
This paper cites Integer programming , volume 271.
How hard is learning to cut? Trade-offs and sample complexity Integer programming , volume 271
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:58.155865Z
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.
Observation 8fa27d63-fae9-441b-b608-4f24e80efd3f · outbound
This paper cites Chv\'atal.
How hard is learning to cut? Trade-offs and sample complexity Chv\'atal
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:57.926258Z
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.
Observation dc4a4fe9-4463-497d-8ea4-db2f2d2ee976 · outbound
This paper cites Sample Complexity of Algorithm Selection Using Neural Networks and Its Applications to Branch-and-Cut.
How hard is learning to cut? Trade-offs and sample complexity Sample Complexity of Algorithm Selection Using Neural Networks and Its Applications to Branch-and-Cut
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T12:19:55.338608Z
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.
Observation de28d8e3-f5d0-472c-891e-f3dfd8df75ff · outbound
This paper cites Rethinking the capacity of graph neural networks for branching strategy.
How hard is learning to cut? Trade-offs and sample complexity Rethinking the capacity of graph neural networks for branching strategy
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:57.717902Z
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.
Observation 59c06e39-e4db-42fa-a465-3499179425ad · outbound
This paper cites Machine Learning for Cutting Planes in Integer Programming: A Survey.
How hard is learning to cut? Trade-offs and sample complexity Machine Learning for Cutting Planes in Integer Programming: A Survey
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:19:53.788524Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2be8d7b1-c638-4a05-833f-a0adf75edc9c · outbound
This paper cites Generalization of erm in stochastic convex optimization: The dimension strikes back.
How hard is learning to cut? Trade-offs and sample complexity Generalization of erm in stochastic convex optimization: The dimension strikes back
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:57.510390Z
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.
Observation c75397b9-7c65-4e43-8f2b-836c7e1bfc85 · outbound
This paper cites an unresolved cited work.
How hard is learning to cut? Trade-offs and sample complexity Unresolved cited work
Reference 10
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:19:57.258876Z
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.
Observation 9293b657-a35c-4fa5-ba0d-8eeabbd15bae · outbound
This paper cites A pac approach to application-specific algorithm selection.
How hard is learning to cut? Trade-offs and sample complexity A pac approach to application-specific algorithm selection
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:57.041721Z
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.
Observation 7cbb236a-ec68-4db3-af3b-58ceaf15df7d · outbound
This paper cites an unresolved cited work.
How hard is learning to cut? Trade-offs and sample complexity Unresolved cited work
Reference 12
- Resolution
- parse uncertain
- raw_fallback, observed 2026-08-07T12:19:56.843931Z
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.
Observation 1a45aa8d-7aac-4f5d-ba9c-0039c4a2a204 · outbound
This paper cites Learning to select cuts for efficient mixed-integer programming.
How hard is learning to cut? Trade-offs and sample complexity Learning to select cuts for efficient mixed-integer programming
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:56.669392Z
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.
Observation 1466ec31-4970-4ec4-98ee-d0c350799fe6 · outbound
This paper cites Mixed integer programming computation.
How hard is learning to cut? Trade-offs and sample complexity Mixed integer programming computation
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:56.479893Z
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.
Observation c3c3a6f8-eff0-4a83-8d80-b5e28771b01c · outbound
This paper cites Learning to stop cut generation for efficient mixed-integer linear programming.
How hard is learning to cut? Trade-offs and sample complexity Learning to stop cut generation for efficient mixed-integer linear programming
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:56.296070Z
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.
Observation 12b74494-cc26-4437-a8f2-168c1a4d49c6 · outbound
This paper cites Algorithms with predictions.
How hard is learning to cut? Trade-offs and sample complexity Algorithms with predictions
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:19:54.532824Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 48fa4827-9a4d-479a-8e5c-c0f0ad65bac5 · outbound
This paper cites Integer and combinatorial optimization , volume 18.
How hard is learning to cut? Trade-offs and sample complexity Integer and combinatorial optimization , volume 18
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:56.164666Z
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.
Observation 2355c4d5-5c27-4d66-a18a-a36f40dba4b5 · outbound
This paper cites Learning to cut by looking ahead: Cutting plane selection via imitation learning.
How hard is learning to cut? Trade-offs and sample complexity Learning to cut by looking ahead: Cutting plane selection via imitation learning
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:56.019804Z
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.
Observation e4172877-05f3-4010-bc09-f8747bd598ec · outbound
This paper cites The algorithm selection problem.
How hard is learning to cut? Trade-offs and sample complexity The algorithm selection problem
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:55.884539Z
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.
Observation 5c0de0c2-f1d4-486a-bd1e-97b678583633 · outbound
This paper cites Theory of Linear and Integer Programming.
How hard is learning to cut? Trade-offs and sample complexity Theory of Linear and Integer Programming
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:55.752834Z
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.
Observation 5e0c61bd-385a-4804-8be9-84eca65e653e · outbound
This paper cites Stochastic convex optimization.
How hard is learning to cut? Trade-offs and sample complexity Stochastic convex optimization
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:55.605868Z
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.
Observation 30a8197d-1364-4898-85bc-06fac2e83dba · outbound
This paper cites Reinforcement learning for integer programming: Learning to cut.
How hard is learning to cut? Trade-offs and sample complexity Reinforcement learning for integer programming: Learning to cut
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:19:55.465262Z
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.
Pith citing papers
Observation 60f8d6a3-cfe3-4405-9d3a-7a8b3d47e7b0 · inbound
Sample Complexity of Stochastic Optimization with Integer Variables cites this paper.
Sample Complexity of Stochastic Optimization with Integer Variables How hard is learning to cut? Trade-offs and sample complexity
Reference 83
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T03:50:58.016781Z
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.