Paper Citation Record · LEDGER

Towards Robust Interpretable Surrogates for Optimization

As of 18 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2412.01264.

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

pith.paper-citation-record.v1
2412.01264 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:41:16.214559Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

41 of 41 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 85c9321a-80ad-4201-9c6b-98b57c11cdc3 · outbound

This paper cites Min--max and min--max regret versions of combinatorial optimization problems: A survey.

Towards Robust Interpretable Surrogates for Optimization Min--max and min--max regret versions of combinatorial optimization problems: A survey

Reference 1

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Observation ffe943b2-eb48-46d8-8def-3160fffc91a5 · outbound

This paper cites Optimal policy trees.

Towards Robust Interpretable Surrogates for Optimization Optimal policy trees

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.851206Z

Source-reported events for the cited work

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Observation 0260257b-6b16-4de8-a258-ca741d8e94a8 · outbound

This paper cites A framework for data-driven explainability in mathematical optimization.

Towards Robust Interpretable Surrogates for Optimization A framework for data-driven explainability in mathematical optimization

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.839592Z

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Observation 02c3737a-b5a7-4b2b-94cf-68f03bd0996d · outbound

This paper cites Strong optimal classification trees.

Towards Robust Interpretable Surrogates for Optimization Strong optimal classification trees

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.827823Z

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Observation 5f0e18d4-cb7a-4246-8293-09cb386db984 · outbound

This paper cites Explainable predict-and-optimize.

Towards Robust Interpretable Surrogates for Optimization Explainable predict-and-optimize

Reference 5

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Observation 2c1da620-f3db-4aa3-a8dd-6b669ff0c2b0 · outbound

This paper cites Decision trees: from efficient prediction to responsible AI.

Towards Robust Interpretable Surrogates for Optimization Decision trees: from efficient prediction to responsible AI

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.800933Z

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Observation 1bfa3258-73cf-4910-b99a-6270c2c942c6 · outbound

This paper cites Interpretable operations research for high-stakes decisions: Designing the Greek COVID -19 testing system.

Towards Robust Interpretable Surrogates for Optimization Interpretable operations research for high-stakes decisions: Designing the Greek COVID -19 testing system

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.789072Z

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Observation d83e366c-81bf-476b-b661-d7c09234cf32 · outbound

This paper cites Improving Stability in Decision Tree Models.

Towards Robust Interpretable Surrogates for Optimization Improving Stability in Decision Tree Models

Reference 8

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This paper cites Optimal prescriptive trees.

Towards Robust Interpretable Surrogates for Optimization Optimal prescriptive trees

Reference 9

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Observation 62d601b8-c420-4967-918d-131a11db0884 · outbound

This paper cites Data-driven robust optimization.

Towards Robust Interpretable Surrogates for Optimization Data-driven robust optimization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.764303Z

Source-reported events for the cited work

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Observation 6d82eb41-1225-4ee3-9d69-d54289c993df · outbound

This paper cites Interpretable clustering: an optimization approach.

Towards Robust Interpretable Surrogates for Optimization Interpretable clustering: an optimization approach

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.752334Z

Source-reported events for the cited work

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Observation 0dfbc796-25f7-436c-aa09-be3db3236d6f · outbound

This paper cites Robust discrete optimization and network flows.

Towards Robust Interpretable Surrogates for Optimization Robust discrete optimization and network flows

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.740904Z

Source-reported events for the cited work

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Observation d65a71a4-0ef9-4e36-9333-7dbbf97f1704 · outbound

This paper cites The price of robustness.

Towards Robust Interpretable Surrogates for Optimization The price of robustness

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.729229Z

Source-reported events for the cited work

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Observation 7df391ff-2da1-4966-92ee-1d8d4a587bed · outbound

This paper cites Algorithms and uncertainty sets for data-driven robust shortest path problems.

Towards Robust Interpretable Surrogates for Optimization Algorithms and uncertainty sets for data-driven robust shortest path problems

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.718157Z

Source-reported events for the cited work

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Observation 1dd6318e-f864-4b8a-9961-302a4bbd9e0e · outbound

This paper cites Explainable interactive evolutionary multiobjective optimization.

Towards Robust Interpretable Surrogates for Optimization Explainable interactive evolutionary multiobjective optimization

Reference 15

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Observation 2c473475-027e-4989-bcc8-18e4762cc1e7 · outbound

This paper cites On clustering and interpreting with rules by means of mathematical optimization.

Towards Robust Interpretable Surrogates for Optimization On clustering and interpreting with rules by means of mathematical optimization

Reference 16

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Observation 428cf85f-9854-4c21-8cd1-f307aeffffa0 · outbound

This paper cites Argumentation for explainable scheduling.

Towards Robust Interpretable Surrogates for Optimization Argumentation for explainable scheduling

Reference 17

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Observation d5ff3bb3-45f4-4bf2-9d94-b2a0e23334dc · outbound

This paper cites Mi s i \'c.

Towards Robust Interpretable Surrogates for Optimization Mi s i \'c

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.672284Z

Source-reported events for the cited work

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Observation 42f1872b-4116-43b3-8e16-4a380a56fdb3 · outbound

This paper cites Costa and Carlos E.

Towards Robust Interpretable Surrogates for Optimization Costa and Carlos E

Reference 19

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Source-reported events for the cited work

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Observation 14b4b06c-b14d-45cc-861b-b48c6f258cc3 · outbound

This paper cites Robust decision trees against adversarial examples.

Towards Robust Interpretable Surrogates for Optimization Robust decision trees against adversarial examples

Reference 20

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Observation 77c89d36-f1f9-4ee0-bc01-22064fe82c11 · outbound

This paper cites predict, then optimize.

Towards Robust Interpretable Surrogates for Optimization predict, then optimize

Reference 21

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Source-reported events for the cited work

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Observation d487b078-f1e1-42aa-ada1-ca7291fafc78 · outbound

This paper cites Explainable data-driven optimization: from context to decision and back again.

Towards Robust Interpretable Surrogates for Optimization Explainable data-driven optimization: from context to decision and back again

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.623832Z

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Observation eb3ea978-57f4-448b-8666-a72217c20513 · outbound

This paper cites A framework for inherently interpretable optimization models.

Towards Robust Interpretable Surrogates for Optimization A framework for inherently interpretable optimization models

Reference 23

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Observation cffda35b-22e6-4adf-a8a1-4d766e36f04a · outbound

This paper cites An Introduction to Robust Combinatorial Optimization.

Towards Robust Interpretable Surrogates for Optimization An Introduction to Robust Combinatorial Optimization

Reference 24

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bfda48a3-3c69-4c08-9a4d-e0f0c34937be · outbound

This paper cites Feature-based interpretable surrogates for optimization.

Towards Robust Interpretable Surrogates for Optimization Feature-based interpretable surrogates for optimization

Reference 25

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Observation 3b9b1e4e-2ae5-49cc-8828-5723ad54ba92 · outbound

This paper cites Garey and David S.

Towards Robust Interpretable Surrogates for Optimization Garey and David S

Reference 26

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Observation fa0cda9e-4070-4aa4-b96c-26d56befba28 · outbound

This paper cites Gurobi Optimizer Reference Manual , 2024.

Towards Robust Interpretable Surrogates for Optimization Gurobi Optimizer Reference Manual , 2024

Reference 27

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Observation 50f366b3-700a-4bb8-932a-15851ff0b04d · outbound

This paper cites Gorissen, \.I hsan Yan ko g lu, and Dick den Hertog.

Towards Robust Interpretable Surrogates for Optimization Gorissen, \.I hsan Yan ko g lu, and Dick den Hertog

Reference 28

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Observation e7881dd7-5d35-4048-a0e7-c27fc99aa6b2 · outbound

This paper cites Hagberg, Daniel A.

Towards Robust Interpretable Surrogates for Optimization Hagberg, Daniel A

Reference 29

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Observation 8fb12c10-0c26-4acb-8f5a-05a326c4e9ad · outbound

This paper cites Optimal robust classification trees.

Towards Robust Interpretable Surrogates for Optimization Optimal robust classification trees

Reference 30

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This paper cites Christopher Beck.

Towards Robust Interpretable Surrogates for Optimization Christopher Beck

Reference 31

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Observation 8fd897d5-a1dd-4b26-a87c-37c9a84706b0 · outbound

This paper cites Counterfactual Explanations for Linear Optimization.

Towards Robust Interpretable Surrogates for Optimization Counterfactual Explanations for Linear Optimization

Reference 32

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Observation ec6290b9-2e44-49bc-b9e0-881cf71e4869 · outbound

This paper cites The multiple-choice knapsack problem.

Towards Robust Interpretable Surrogates for Optimization The multiple-choice knapsack problem

Reference 33

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Observation f02cff52-1502-4683-ab0f-56e1a830c035 · outbound

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Towards Robust Interpretable Surrogates for Optimization Unresolved cited work

Reference 34

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Source-reported events for the cited work

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Observation d0a32d09-67bc-4f13-9131-9e4cbbd6da04 · outbound

This paper cites Robust decision trees for the multi-mode project scheduling problem with a resource investment objective and uncertain activity duration.

Towards Robust Interpretable Surrogates for Optimization Robust decision trees for the multi-mode project scheduling problem with a resource investment objective and uncertain activity duration

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.492159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 94e24c5e-79f4-4266-944f-7135c35d6b96 · outbound

This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.

Towards Robust Interpretable Surrogates for Optimization Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Reference 36

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Source-reported events for the cited work

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Observation 9a4b2938-69c0-43c6-8fd4-8c7d1c621db7 · outbound

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Towards Robust Interpretable Surrogates for Optimization Zoltners

Reference 37

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Observation 4b3e4a7b-028a-42aa-ad5e-96a468ef0882 · outbound

This paper cites Explaining solutions to multi-stage stochastic optimization problems to decision makers.

Towards Robust Interpretable Surrogates for Optimization Explaining solutions to multi-stage stochastic optimization problems to decision makers

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.455183Z

Source-reported events for the cited work

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Observation e77f2832-9c6e-499a-ac12-c3ed3a1f27d9 · outbound

This paper cites Efficient training of robust decision trees against adversarial examples.

Towards Robust Interpretable Surrogates for Optimization Efficient training of robust decision trees against adversarial examples

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.442728Z

Source-reported events for the cited work

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Observation 1fff7b6c-e496-4d05-a5ff-894bcd70b130 · outbound

This paper cites Robust optimal classification trees against adversarial examples.

Towards Robust Interpretable Surrogates for Optimization Robust optimal classification trees against adversarial examples

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.430336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 44d83737-b42f-4f26-9ef8-fa6ae24683c8 · outbound

This paper cites Solving two-stage robust optimization problems using a column-and-constraint generation method.

Towards Robust Interpretable Surrogates for Optimization Solving two-stage robust optimization problems using a column-and-constraint generation method

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:41:16.418727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

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