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Paper Citation Record · LEDGER

MOSS: Multi-Objective Optimization for Stable Rule Sets

As of 17 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2506.08030.

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

pith.paper-citation-record.v1
2506.08030 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:53:58.903434Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

38 of 38 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a217f950-7298-4520-8cea-1bc189357ca2 · outbound

This paper cites Version 9.0.,.

MOSS: Multi-Objective Optimization for Stable Rule Sets Version 9.0.,

Reference 1

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Observation 2a689946-7005-4291-a4c0-3a70fd2315de · outbound

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MOSS: Multi-Objective Optimization for Stable Rule Sets Unresolved cited work

Reference 2

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Observation 8e59ae46-bd16-49dd-bb15-db49c5c88696 · outbound

This paper cites Sirus: Stable and interpretable rule set for classification.Electronic Journal of Statistics, 15:427–505, 2021.

MOSS: Multi-Objective Optimization for Stable Rule Sets Sirus: Stable and interpretable rule set for classification.Electronic Journal of Statistics, 15:427–505, 2021

Reference 3

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Observation 9867da6f-9ace-4da1-9912-6a9a19cc3bca · outbound

This paper cites Sparse high-dimensional regression.The Annals of Statistics, 48(1):300–323, 2020.

MOSS: Multi-Objective Optimization for Stable Rule Sets Sparse high-dimensional regression.The Annals of Statistics, 48(1):300–323, 2020

Reference 4

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Source-reported events for the cited work

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Observation a7548cf0-143f-454a-a9b9-e0de8817b891 · outbound

This paper cites Sparse classification: a scalable discrete optimization perspective.Machine Learning, 110:3177–3209, 2021.

MOSS: Multi-Objective Optimization for Stable Rule Sets Sparse classification: a scalable discrete optimization perspective.Machine Learning, 110:3177–3209, 2021

Reference 5

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Source-reported events for the cited work

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Observation 288f57a3-9724-4163-95e1-283d17864965 · outbound

This paper cites Cambridge university press, 2004.

MOSS: Multi-Objective Optimization for Stable Rule Sets Cambridge university press, 2004

Reference 6

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Observation 10d9b23d-8075-4e50-88a2-6c37fb3172cc · outbound

This paper cites Random forests.Machine learning, 45:5–32, 2001.

MOSS: Multi-Objective Optimization for Stable Rule Sets Random forests.Machine learning, 45:5–32, 2001

Reference 7

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Observation cd7c1729-13ab-4569-af60-820f7a4d1941 · outbound

This paper cites API design for machine learning software: experiences from the scikit-learn project.

MOSS: Multi-Objective Optimization for Stable Rule Sets API design for machine learning software: experiences from the scikit-learn project

Reference 8

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Source-reported events for the cited work

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Observation b105a66d-f628-4bd7-9684-449e90c32307 · outbound

This paper cites Efficient exploration of the rashomon set of rule-set models.

MOSS: Multi-Objective Optimization for Stable Rule Sets Efficient exploration of the rashomon set of rule-set models

Reference 9

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Observation 9519d9ee-6d91-4da9-ad6d-3f53557faaf8 · outbound

This paper cites An outer-approximation algorithm for a class of mixed-integer nonlinear programs.Mathematical programming, 36: 307–339, 1986.

MOSS: Multi-Objective Optimization for Stable Rule Sets An outer-approximation algorithm for a class of mixed-integer nonlinear programs.Mathematical programming, 36: 307–339, 1986

Reference 10

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Observation bb32470b-36d4-4f66-bad6-c6d8986a1f0d · outbound

This paper cites A tutorial on multiobjective op- timization: fundamentals and evolutionary methods.Natural computing, 17: 585–609, 2018.

MOSS: Multi-Objective Optimization for Stable Rule Sets A tutorial on multiobjective op- timization: fundamentals and evolutionary methods.Natural computing, 17: 585–609, 2018

Reference 11

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Observation d5a35721-aab4-42f0-8ae9-ef69d5fc6c74 · outbound

This paper cites The low response score (lrs) a metric to locate, predict, and manage hard-to-survey populations.Public Opinion Quarterly, 81 (1):144–156, 2017.

MOSS: Multi-Objective Optimization for Stable Rule Sets The low response score (lrs) a metric to locate, predict, and manage hard-to-survey populations.Public Opinion Quarterly, 81 (1):144–156, 2017

Reference 12

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Source-reported events for the cited work

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Observation b0d63729-2b14-4d49-96fb-4a8cc7221337 · outbound

This paper cites Solving mixed integer nonlinear programs by outer approximation.Mathematical programming, 66:327–349, 1994.

MOSS: Multi-Objective Optimization for Stable Rule Sets Solving mixed integer nonlinear programs by outer approximation.Mathematical programming, 66:327–349, 1994

Reference 13

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Observation 4c9e2590-8851-4274-a7ba-aed92581eeaa · outbound

This paper cites Predictive learning via rule ensembles.

MOSS: Multi-Objective Optimization for Stable Rule Sets Predictive learning via rule ensembles

Reference 14

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Observation 3ab732a7-c223-414c-bf30-919882005204 · outbound

This paper cites Seabird trophic position across three ocean regions tracks ecosystem differences.Frontiers in Marine Science, 5:317, 2018.

MOSS: Multi-Objective Optimization for Stable Rule Sets Seabird trophic position across three ocean regions tracks ecosystem differences.Frontiers in Marine Science, 5:317, 2018

Reference 15

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Observation b8b77e71-82a6-47a8-8e44-16cc58214123 · outbound

This paper cites Gurobi Optimizer Reference Manual, 2024.

MOSS: Multi-Objective Optimization for Stable Rule Sets Gurobi Optimizer Reference Manual, 2024

Reference 16

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MOSS: Multi-Objective Optimization for Stable Rule Sets Unresolved cited work

Reference 17

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Observation b043e9c4-1b07-471a-ab53-4b977721374e · outbound

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MOSS: Multi-Objective Optimization for Stable Rule Sets An introduction to glmnet

Reference 18

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This paper cites L0learn: A scalable package for sparse learning using l0 regularization.Journal of Machine Learning Research, 24(205):1–8, 2023.

MOSS: Multi-Objective Optimization for Stable Rule Sets L0learn: A scalable package for sparse learning using l0 regularization.Journal of Machine Learning Research, 24(205):1–8, 2023

Reference 19

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Observation 217f4385-b7af-4f75-8e56-ee39404e60fe · outbound

This paper cites The 2020 census & the environment: How census data are used for environmental justice & climate action.

MOSS: Multi-Objective Optimization for Stable Rule Sets The 2020 census & the environment: How census data are used for environmental justice & climate action

Reference 20

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This paper cites Fire: An optimization approach for fast inter- pretable rule extraction.

MOSS: Multi-Objective Optimization for Stable Rule Sets Fire: An optimization approach for fast inter- pretable rule extraction

Reference 21

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Observation f737874e-c2f3-41f3-833c-5f388ad910a4 · outbound

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MOSS: Multi-Objective Optimization for Stable Rule Sets Unresolved cited work

Reference 22

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This paper cites Subset selection with shrinkage: Sparse linear modeling when the snr is low.Operations Research, 71 (1):129–147, 2023.

MOSS: Multi-Objective Optimization for Stable Rule Sets Subset selection with shrinkage: Sparse linear modeling when the snr is low.Operations Research, 71 (1):129–147, 2023

Reference 23

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Observation 23c968ae-7909-416a-9bb5-2a452e63e030 · outbound

This paper cites Computing the Collection of Good Models for Rule Lists.

MOSS: Multi-Objective Optimization for Stable Rule Sets Computing the Collection of Good Models for Rule Lists

Reference 24

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This paper cites Definitions, methods, and applications in interpretable machine learning.

MOSS: Multi-Objective Optimization for Stable Rule Sets Definitions, methods, and applications in interpretable machine learning

Reference 25

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This paper cites Stability selection.Journal of the Royal Statistical Society Series B: Statistical Methodology, 72(4):417–473, 2010.

MOSS: Multi-Objective Optimization for Stable Rule Sets Stability selection.Journal of the Royal Statistical Society Series B: Statistical Methodology, 72(4):417–473, 2010

Reference 26

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MOSS: Multi-Objective Optimization for Stable Rule Sets Routledge, 2005

Reference 27

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This paper cites On the stability of feature selection algorithms.Journal of Machine Learning Research, 18(174):1–54, 2018.

MOSS: Multi-Objective Optimization for Stable Rule Sets On the stability of feature selection algorithms.Journal of Machine Learning Research, 18(174):1–54, 2018

Reference 28

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MOSS: Multi-Objective Optimization for Stable Rule Sets Census Bureau

Reference 29

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This paper cites Algorithms for interpretable machine learning.

MOSS: Multi-Objective Optimization for Stable Rule Sets Algorithms for interpretable machine learning

Reference 30

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MOSS: Multi-Objective Optimization for Stable Rule Sets Generalized linear rule models

Reference 31

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This paper cites van Rijn, Bernd Bischl, and Luis Torgo.

MOSS: Multi-Objective Optimization for Stable Rule Sets van Rijn, Bernd Bischl, and Luis Torgo

Reference 32

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MOSS: Multi-Objective Optimization for Stable Rule Sets Stability

Reference 33

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Observation 45925792-61ef-4cd9-9ac3-32be1d5f2648 · outbound

This paper cites Exploring the whole rashomon set of sparse decision trees.Advances in neural information processing systems, 35:14071–14084, 2022.

MOSS: Multi-Objective Optimization for Stable Rule Sets Exploring the whole rashomon set of sparse decision trees.Advances in neural information processing systems, 35:14071–14084, 2022

Reference 34

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Source-reported events for the cited work

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Observation de830013-21d1-45ca-9e58-83b614a1a1ca · outbound

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MOSS: Multi-Objective Optimization for Stable Rule Sets Veridical data science

Reference 35

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raw_fallback, observed 2026-08-07T11:53:59.304019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T11:53:58.821020Z digest=sha256:98ac6d76940c424558353fea7cc3fbe773812e9b46f78ee58aeca5765b7f9835

Observation d4fe08ae-c630-4aa8-98cb-8e9fc144670e · outbound

This paper cites Three principles of data science: predictability, computability, and stability (pcs).

MOSS: Multi-Objective Optimization for Stable Rule Sets Three principles of data science: predictability, computability, and stability (pcs)

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:53:59.540734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T11:53:58.750491Z digest=sha256:66f107a83db9d99bf20c210d99a755c44d833e2f23f9e57c83dfa3030a05154f

Observation 4a6372c3-ab54-45bc-91ee-bdf6705cd73c · outbound

This paper cites We take the scenario with the lower objective value as the solution for Problem 3.

MOSS: Multi-Objective Optimization for Stable Rule Sets We take the scenario with the lower objective value as the solution for Problem 3

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:53:59.123470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T11:53:58.903434Z digest=sha256:e2481b4ce5a2ceff890f148530393aa1a7f4c31e87f386fffd6cb201662ec697

Observation 3fee0025-098d-460b-85dc-b734f7260800 · outbound

This paper cites an unresolved cited work.

MOSS: Multi-Objective Optimization for Stable Rule Sets Unresolved cited work

Reference 2019

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:54:05.183041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T11:53:55.559257Z digest=sha256:569d2bb20049131ee63eeced2f9a466f2dea291c29d86eadbdd8ff45454c994e

Pith citing papers

No inbound Pith citation observations are available.