Paper Citation Record · LEDGER

Improving Model Classification by Optimizing the Training Dataset

As of 23 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2507.16729.

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

pith.paper-citation-record.v1
2507.16729 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:09:53.203663Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T08:41:11.951712Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T10:39:44.941742Z

Reference resolution

42 of 42 outbound references displayed

verified exact2
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unresolved5
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 69dca9ee-78a9-4f67-879c-be9fbe93173f · outbound

This paper cites Physical Unclonable Functions.

Improving Model Classification by Optimizing the Training Dataset Physical Unclonable Functions

Reference 1

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Observation d264e03e-e1a3-44ef-b895-afa170b809bd · outbound

This paper cites The power of uniform sampling for coresets.

Improving Model Classification by Optimizing the Training Dataset The power of uniform sampling for coresets

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:09:53.963929Z

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Observation 3e4fc78f-0050-48eb-af0e-dff78f8237b3 · outbound

This paper cites New Frameworks for Offline and Streaming Coreset Constructions.

Improving Model Classification by Optimizing the Training Dataset New Frameworks for Offline and Streaming Coreset Constructions

Reference 3

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Observation c4232541-cbee-41f8-896c-c8e598fd4572 · outbound

This paper cites an unresolved cited work.

Improving Model Classification by Optimizing the Training Dataset Unresolved cited work

Reference 4

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Observation 58538770-be2d-42fe-b3b5-6d7735eeb0c9 · outbound

This paper cites Data-dependent coresets for compressing neural networks with applications to generalization bounds.

Improving Model Classification by Optimizing the Training Dataset Data-dependent coresets for compressing neural networks with applications to generalization bounds

Reference 5

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Observation f797f045-c3cb-4ada-b533-5080b5ef5abc · outbound

This paper cites Improved coresets for euclidean k -means.

Improving Model Classification by Optimizing the Training Dataset Improved coresets for euclidean k -means

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:09:53.913702Z

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Observation f78e1f22-861a-45ac-a3f1-b59478b47eb1 · outbound

This paper cites XGBoost : A scalable tree boosting system.

Improving Model Classification by Optimizing the Training Dataset XGBoost : A scalable tree boosting system

Reference 7

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Source-reported events for the cited work

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Observation 5b08583d-3892-4b2a-a6f2-e841fedb873e · outbound

This paper cites Xgboost: A scalable tree boosting system.

Improving Model Classification by Optimizing the Training Dataset Xgboost: A scalable tree boosting system

Reference 8

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Source-reported events for the cited work

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Observation bd02523d-60e7-4ed2-80e3-d467ec8aff74 · outbound

This paper cites Libsvm: A library for support vector machines.

Improving Model Classification by Optimizing the Training Dataset Libsvm: A library for support vector machines

Reference 9

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Observation 70e2c8e9-3c37-48cc-98f4-dbb338ab69da · outbound

This paper cites Lp row sampling by lewis weights.

Improving Model Classification by Optimizing the Training Dataset Lp row sampling by lewis weights

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:09:53.853700Z

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Observation 6adb056c-9044-49fa-8aca-eb537b4da0cf · outbound

This paper cites Dataheroes: Automated framework for ml training set optimization and refinement, 2022.

Improving Model Classification by Optimizing the Training Dataset Dataheroes: Automated framework for ml training set optimization and refinement, 2022

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:09:53.837305Z

Source-reported events for the cited work

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Observation 5cd37367-5b22-4ffe-abf7-67f15a563060 · outbound

This paper cites Coreset-based neural network compression.

Improving Model Classification by Optimizing the Training Dataset Coreset-based neural network compression

Reference 12

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Source-reported events for the cited work

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Observation 8127fe56-0381-477d-8545-03947d8d0b99 · outbound

This paper cites Calibrating probability with undersampling for unbalanced classification.

Improving Model Classification by Optimizing the Training Dataset Calibrating probability with undersampling for unbalanced classification

Reference 13

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Observation e0181ee5-11d7-4948-87ef-ceda647095e9 · outbound

This paper cites Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors).

Improving Model Classification by Optimizing the Training Dataset Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:09:53.784353Z

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Observation 0340ede6-9317-47ea-8c79-44d71fd667d6 · outbound

This paper cites Greedy function approximation: a gradient boosting machine.

Improving Model Classification by Optimizing the Training Dataset Greedy function approximation: a gradient boosting machine

Reference 15

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Observation 6ba7aeb7-c876-42db-8fae-6a9a20d5a4d4 · outbound

This paper cites Turning big data into tiny data: Constant-size coresets for k-means, pca, and projective clustering.

Improving Model Classification by Optimizing the Training Dataset Turning big data into tiny data: Constant-size coresets for k-means, pca, and projective clustering

Reference 16

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Observation f7ec79de-7a32-4327-9bca-90619dba0d3e · outbound

This paper cites Deepcore: A comprehensive library for coreset selection in deep learning.

Improving Model Classification by Optimizing the Training Dataset Deepcore: A comprehensive library for coreset selection in deep learning

Reference 17

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Observation 585de5b2-c306-4009-b36b-0cc160aef144 · outbound

This paper cites Harris, K.

Improving Model Classification by Optimizing the Training Dataset Harris, K

Reference 18

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Source-reported events for the cited work

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Observation 885a0946-7602-4b97-8be7-7581770b7e7b · outbound

This paper cites Ieee-cis fraud detection.

Improving Model Classification by Optimizing the Training Dataset Ieee-cis fraud detection

Reference 19

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Source-reported events for the cited work

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Observation 4d67b94b-606b-42b0-9412-2999e8ec5c3a · outbound

This paper cites Finite dimensional subspaces of lp.

Improving Model Classification by Optimizing the Training Dataset Finite dimensional subspaces of lp

Reference 20

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Observation 3f2042e0-bd29-4b95-8fae-0339954b9d3f · outbound

This paper cites Coresets for decision trees of signals.

Improving Model Classification by Optimizing the Training Dataset Coresets for decision trees of signals

Reference 21

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Observation b07c2ccd-7ce4-4dbe-b6e5-1e2c259a2043 · outbound

This paper cites Sets clustering.

Improving Model Classification by Optimizing the Training Dataset Sets clustering

Reference 22

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Observation ffbc465a-ee7f-4c87-9be7-d0a2a4b7fade · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Improving Model Classification by Optimizing the Training Dataset UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 23

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Observation e185abcd-c2bc-44f0-b312-07408e1d5fc9 · outbound

This paper cites p-generalized probit regression and scalable maximum likelihood estimation via sketching and coresets.

Improving Model Classification by Optimizing the Training Dataset p-generalized probit regression and scalable maximum likelihood estimation via sketching and coresets

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:09:53.625797Z

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Observation 0e18863a-ca6a-403a-ada6-1a4d2eae62cc · outbound

This paper cites On coresets for logistic regression.

Improving Model Classification by Optimizing the Training Dataset On coresets for logistic regression

Reference 25

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Source-reported events for the cited work

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Observation 69e23ce9-887a-4cfc-ba62-a7f0c89ce472 · outbound

This paper cites Autocoreset: an automatic practical coreset construction framework.

Improving Model Classification by Optimizing the Training Dataset Autocoreset: an automatic practical coreset construction framework

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:09:53.591058Z

Source-reported events for the cited work

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Observation 97f90177-812d-4bcb-8933-74ff7eaf6abb · outbound

This paper cites Coresets for data discretization and sine wave fitting.

Improving Model Classification by Optimizing the Training Dataset Coresets for data discretization and sine wave fitting

Reference 27

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Observation 1f9a79c6-f349-4f12-bd79-44470b5015f9 · outbound

This paper cites Early stopping-but when? In Neural Networks: Tricks of the trade , pages 55--69.

Improving Model Classification by Optimizing the Training Dataset Early stopping-but when? In Neural Networks: Tricks of the trade , pages 55--69

Reference 28

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Observation 4e4b35b0-098a-4f72-a8ae-e3a9fd21072e · outbound

This paper cites Scikit-learn: Machine learning in python.

Improving Model Classification by Optimizing the Training Dataset Scikit-learn: Machine learning in python

Reference 29

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Source-reported events for the cited work

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Observation a40a5860-f814-44d2-9dad-9f87861d877c · outbound

This paper cites An efficient drifters deployment strategy to evaluate water current velocity fields.

Improving Model Classification by Optimizing the Training Dataset An efficient drifters deployment strategy to evaluate water current velocity fields

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:09:53.520215Z

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Observation de3eb625-0403-4ff6-8b16-5912511810e2 · outbound

This paper cites On coresets for support vector machines.

Improving Model Classification by Optimizing the Training Dataset On coresets for support vector machines

Reference 31

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Source-reported events for the cited work

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Observation 0487913c-a2d7-4879-8ea2-5650f83d320c · outbound

This paper cites Generic coreset for scalable learning of monotonic kernels: Logistic regression, sigmoid and more.

Improving Model Classification by Optimizing the Training Dataset Generic coreset for scalable learning of monotonic kernels: Logistic regression, sigmoid and more

Reference 32

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Observation 84da711b-975f-48bc-af83-5c02c951644f · outbound

This paper cites Coresets for near-convex functions.

Improving Model Classification by Optimizing the Training Dataset Coresets for near-convex functions

Reference 33

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Source-reported events for the cited work

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Observation e7c22004-79be-4db4-a383-98f35bd5bc47 · outbound

This paper cites Pruning neural networks via coresets and convex geometry: Towards no assumptions.

Improving Model Classification by Optimizing the Training Dataset Pruning neural networks via coresets and convex geometry: Towards no assumptions

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:09:53.445967Z

Source-reported events for the cited work

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Observation da94012b-d60a-4666-9679-67dcc7ff1432 · outbound

This paper cites New coresets for projective clustering and applications.

Improving Model Classification by Optimizing the Training Dataset New coresets for projective clustering and applications

Reference 35

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Observation 9888117f-484d-4ae9-a51f-e18633753438 · outbound

This paper cites Provable data subset selection for efficient neural networks training.

Improving Model Classification by Optimizing the Training Dataset Provable data subset selection for efficient neural networks training

Reference 36

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Source-reported events for the cited work

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Observation 37ef526e-99c1-45e9-b2fe-5db0efde15a2 · outbound

This paper cites Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St \'e fan J.

Improving Model Classification by Optimizing the Training Dataset Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St \'e fan J

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:09:53.385460Z

Source-reported events for the cited work

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Observation 5857de4a-4385-4a5e-9d67-4a1e1e7a7165 · outbound

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Improving Model Classification by Optimizing the Training Dataset Unresolved cited work

Reference 38

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Observation 7fdace38-3d34-4ff1-9b5c-865816c4807d · outbound

This paper cites A near-linear algorithm for projective clustering integer points.

Improving Model Classification by Optimizing the Training Dataset A near-linear algorithm for projective clustering integer points

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:09:53.349753Z

Source-reported events for the cited work

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Observation 4a9b3a97-56d1-49a9-9500-635350577773 · outbound

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Improving Model Classification by Optimizing the Training Dataset Unresolved cited work

Reference 40

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Observation faa950a5-4e8a-45a2-b253-70c079fa28b0 · outbound

This paper cites Automated filtering of human feedback data for aligning text-to-image diffusion models.

Improving Model Classification by Optimizing the Training Dataset Automated filtering of human feedback data for aligning text-to-image diffusion models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:09:53.330279Z

Source-reported events for the cited work

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Observation f8daa681-00a2-49a8-8cc0-8dd08a93f915 · outbound

This paper cites Lima: Less is more for alignment.

Improving Model Classification by Optimizing the Training Dataset Lima: Less is more for alignment

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:09:53.312875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation f3a71f56-ac4a-4f5c-bd54-b59449cae8e1 · inbound

ARIA: Adaptive Region-Based Importance Allocation for Conditional Diffusion Distillation cites this paper.

ARIA: Adaptive Region-Based Importance Allocation for Conditional Diffusion Distillation Improving Model Classification by Optimizing the Training Dataset

Reference 56

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.