Paper Citation Record · LEDGER

Efficient computation of counterfactual explanations of LVQ models

As of 15 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:1908.00735.

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

pith.paper-citation-record.v1
1908.00735 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:39:39.428979Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

40 of 40 outbound references displayed

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External citation measurements

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Outbound references

Observation ceb4e779-e9ab-41f0-be25-e4a2c5c7c285 · outbound

This paper cites Rao, and Ravi Shroﬀ.

Efficient computation of counterfactual explanations of LVQ models Rao, and Ravi Shroﬀ

Reference 1

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This paper cites How algorithms can bring down minorities ’ credit scores.

Efficient computation of counterfactual explanations of LVQ models How algorithms can bring down minorities ’ credit scores

Reference 2

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Observation 2f16ba5d-8950-493f-8994-3b8b39dd1fe8 · outbound

This paper cites Khandani, Adlar J.

Efficient computation of counterfactual explanations of LVQ models Khandani, Adlar J

Reference 3

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This paper cites General data protecti on regulation.

Efficient computation of counterfactual explanations of LVQ models General data protecti on regulation

Reference 4

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This paper cites Gilpin, David Bau, Ben Z.

Efficient computation of counterfactual explanations of LVQ models Gilpin, David Bau, Ben Z

Reference 5

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This paper cites A survey of methods for explaining black box models.

Efficient computation of counterfactual explanations of LVQ models A survey of methods for explaining black box models

Reference 6

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Observation 662d754f-2f1e-47a0-95a1-4eedd1e9ee44 · outbound

This paper cites A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI.

Efficient computation of counterfactual explanations of LVQ models A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI

Reference 7

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This paper cites Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models.

Efficient computation of counterfactual explanations of LVQ models Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

Reference 8

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This paper cites Interpretable Machine Learning.

Efficient computation of counterfactual explanations of LVQ models Interpretable Machine Learning

Reference 9

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Observation f0eedb69-40d8-42fe-854d-88ab495b37d5 · outbound

This paper cites Model-agnostic interpretability of machine learning.

Efficient computation of counterfactual explanations of LVQ models Model-agnostic interpretability of machine learning

Reference 10

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Observation d3c642e6-b1bc-4fa7-8346-0ed80efbd1c9 · outbound

This paper cites A Simple and Effective Model-Based Variable Importance Measure.

Efficient computation of counterfactual explanations of LVQ models A Simple and Effective Model-Based Variable Importance Measure

Reference 11

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Observation 3f2dd07f-a24c-4187-9768-059da46f0eb5 · outbound

This paper cites All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously.

Efficient computation of counterfactual explanations of LVQ models All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Reference 12

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Observation f4ec0c2f-d44a-44ae-8e89-fcdc3799f9fb · outbound

This paper cites Causal interpretatio ns of black-box models.

Efficient computation of counterfactual explanations of LVQ models Causal interpretatio ns of black-box models

Reference 13

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This paper cites ”why should i trust you?”: Explaining the predictions of any classiﬁer.

Efficient computation of counterfactual explanations of LVQ models ”why should i trust you?”: Explaining the predictions of any classiﬁer

Reference 14

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This paper cites Local Rule-Based Explanations of Black Box Decision Systems.

Efficient computation of counterfactual explanations of LVQ models Local Rule-Based Explanations of Black Box Decision Systems

Reference 15

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This paper cites Aamodt and E.

Efficient computation of counterfactual explanations of LVQ models Aamodt and E

Reference 16

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Observation 948d673d-fba4-4ebf-9d87-2d22ceba0482 · outbound

This paper cites Example s are not enough, learn to criticize! criticism for interpretability.

Efficient computation of counterfactual explanations of LVQ models Example s are not enough, learn to criticize! criticism for interpretability

Reference 17

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Observation e0be3577-7f19-45ce-925c-faaae29b6c82 · outbound

This paper cites Understanding black-box p redictions via inﬂuence func- tions.

Efficient computation of counterfactual explanations of LVQ models Understanding black-box p redictions via inﬂuence func- tions

Reference 18

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Observation 885cd7fb-fd8d-46f6-b602-e1185b018a63 · outbound

This paper cites Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR.

Efficient computation of counterfactual explanations of LVQ models Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR

Reference 19

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This paper cites CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models.

Efficient computation of counterfactual explanations of LVQ models CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models

Reference 20

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This paper cites Comparison-based inverse classiﬁcation for i nterpretability in machine learn- ing.

Efficient computation of counterfactual explanations of LVQ models Comparison-based inverse classiﬁcation for i nterpretability in machine learn- ing

Reference 21

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This paper cites Est´ evez.

Efficient computation of counterfactual explanations of LVQ models Est´ evez

Reference 22

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Observation 65c9d6d0-a65e-4adb-aa42-3e90cfe4cefb · outbound

This paper cites Ad aptive relevance matrices in learning vector quantization.

Efficient computation of counterfactual explanations of LVQ models Ad aptive relevance matrices in learning vector quantization

Reference 23

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This paper cites Di stance learning in discriminative vector quantization.

Efficient computation of counterfactual explanations of LVQ models Di stance learning in discriminative vector quantization

Reference 24

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This paper cites A life-long learning vector quantization approach for interactive lea rning of multiple categories.

Efficient computation of counterfactual explanations of LVQ models A life-long learning vector quantization approach for interactive lea rning of multiple categories

Reference 25

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Observation ea46279f-62c4-40f2-b6b4-6f44e0d83d83 · outbound

This paper cites Interpretable Counterfactual Explanations Guided by Prototypes.

Efficient computation of counterfactual explanations of LVQ models Interpretable Counterfactual Explanations Guided by Prototypes

Reference 26

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This paper cites FACE: Feasible and Actionable Counterfactual Explanations.

Efficient computation of counterfactual explanations of LVQ models FACE: Feasible and Actionable Counterfactual Explanations

Reference 27

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Efficient computation of counterfactual explanations of LVQ models Convex Optimization

Reference 28

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This paper cites General Heuristics for Nonconvex Quadratically Constrained Quadratic Programming.

Efficient computation of counterfactual explanations of LVQ models General Heuristics for Nonconvex Quadratically Constrained Quadratic Programming

Reference 29

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This paper cites Mangasarian William H.

Efficient computation of counterfactual explanations of LVQ models Mangasarian William H

Reference 30

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Efficient computation of counterfactual explanations of LVQ models Alpaydin and C

Reference 31

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This paper cites Ames, iowa: Alternative to the boston hous ing data as an end of semester regression project.

Efficient computation of counterfactual explanations of LVQ models Ames, iowa: Alternative to the boston hous ing data as an end of semester regression project

Reference 32

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This paper cites Ceml: Counterfactuals for explaining m achine learning models - a python toolbox.

Efficient computation of counterfactual explanations of LVQ models Ceml: Counterfactuals for explaining m achine learning models - a python toolbox

Reference 33

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This paper cites Python tutorial.

Efficient computation of counterfactual explanations of LVQ models Python tutorial

Reference 34

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This paper cites CVXPY: A Python-embed ded modeling language for convex optimization.

Efficient computation of counterfactual explanations of LVQ models CVXPY: A Python-embed ded modeling language for convex optimization

Reference 35

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This paper cites A cvxpy extension for han dling nonconvex qcqp via suggest-and-improve framework.

Efficient computation of counterfactual explanations of LVQ models A cvxpy extension for han dling nonconvex qcqp via suggest-and-improve framework

Reference 36

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Observation a955cd97-d442-40a1-89a1-5b851b6cbfc6 · outbound

This paper cites sklearn-lvq.

Efficient computation of counterfactual explanations of LVQ models sklearn-lvq

Reference 37

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This paper cites Chris Colbert, and Ga¨ el Varoqu aux.

Efficient computation of counterfactual explanations of LVQ models Chris Colbert, and Ga¨ el Varoqu aux

Reference 38

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This paper cites Sci Py: Open source scientiﬁc tools for Python, 2001–.

Efficient computation of counterfactual explanations of LVQ models Sci Py: Open source scientiﬁc tools for Python, 2001–

Reference 39

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Observation e85d0a3b-f48b-498a-9994-1e30f9e4c6cc · outbound

This paper cites Pedregosa, G.

Efficient computation of counterfactual explanations of LVQ models Pedregosa, G

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:39:39.620917Z

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