Paper Citation Record · LEDGER

How to Achieve Higher Accuracy with Less Training Points?

As of 23 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 1 inbound Pith citation observation for arXiv:2504.13586.

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

pith.paper-citation-record.v1
2504.13586 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:07:58.663896Z

measured 25 of 25 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 2026-06-26T16:53:53.441274Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T04:39:34.368247Z

Reference resolution

24 of 24 outbound references displayed

verified exact1
verified fuzzy16
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4e9c692f-398a-41bb-a0b4-4d7b6d51b551 · outbound

This paper cites Ahmad, R.

How to Achieve Higher Accuracy with Less Training Points? Ahmad, R

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.932784Z

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.

Observation 27c68b36-d39c-43da-ac74-13c1e6b75c10 · outbound

This paper cites Green ai,.

How to Achieve Higher Accuracy with Less Training Points? Green ai,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.921059Z

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.

Observation 14cb5ba3-6045-4a25-b727-2cc45f24478d · outbound

This paper cites A systematic review of green ai,.

How to Achieve Higher Accuracy with Less Training Points? A systematic review of green ai,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.910261Z

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.

Observation cf73320e-8527-4197-a860-021969299da6 · outbound

This paper cites Toward green ai: A methodological survey of the scientiﬁc literature,.

How to Achieve Higher Accuracy with Less Training Points? Toward green ai: A methodological survey of the scientiﬁc literature,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.900058Z

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.

Observation 04e93516-dbb8-4692-9871-5a66e6527cee · outbound

This paper cites An Empirical Comparison of Instance Attribution Methods for NLP.

How to Achieve Higher Accuracy with Less Training Points? An Empirical Comparison of Instance Attribution Methods for NLP

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T12:07:58.594433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7296619-0eab-4647-b420-35098e0e2051 · outbound

This paper cites Iaeval: A comprehensive evaluation of in- stance attribution on natural language understanding,.

How to Achieve Higher Accuracy with Less Training Points? Iaeval: A comprehensive evaluation of in- stance attribution on natural language understanding,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.890071Z

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.

Observation 5d826adb-c8b8-4cd3-bc4e-8a0eb1251ad3 · outbound

This paper cites Machine learning explainability in ﬁnance: an application to default risk analysis,.

How to Achieve Higher Accuracy with Less Training Points? Machine learning explainability in ﬁnance: an application to default risk analysis,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.880602Z

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.

Observation 970f42ea-2efc-479e-9f11-87fb5b961d09 · outbound

This paper cites An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference?.

How to Achieve Higher Accuracy with Less Training Points? An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference?

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T12:07:58.606269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca7bf6ca-c796-4dd6-9b79-7a3db855dac2 · outbound

This paper cites Explainable machine learning for pub- lic policy: Use cases, gaps, and research directions,.

How to Achieve Higher Accuracy with Less Training Points? Explainable machine learning for pub- lic policy: Use cases, gaps, and research directions,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.869536Z

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.

Observation 02f46094-338b-4d78-8411-42c9db0cb4e4 · outbound

This paper cites Understanding black-box predictions via inﬂuence functions,.

How to Achieve Higher Accuracy with Less Training Points? Understanding black-box predictions via inﬂuence functions,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.858832Z

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.

Observation 754fb802-294c-40e2-90f0-ea58189ed365 · outbound

This paper cites Debugging Tests for Model Explanations.

How to Achieve Higher Accuracy with Less Training Points? Debugging Tests for Model Explanations

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T12:07:58.618141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0bd0200-501c-4931-bcbe-ea8674f3db22 · outbound

This paper cites Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions.

How to Achieve Higher Accuracy with Less Training Points? Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T12:07:58.621935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7baed70b-9bce-4806-a7c3-3df897cad2c9 · outbound

This paper cites Interactive label cleaning with example- based explanations,.

How to Achieve Higher Accuracy with Less Training Points? Interactive label cleaning with example- based explanations,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.847804Z

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.

Observation 5485e186-8419-4c1c-9bb0-96886be3223a · outbound

This paper cites an unresolved cited work.

How to Achieve Higher Accuracy with Less Training Points? Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-16T12:07:58.836965Z

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.

Observation dae6e0cb-31d8-4bd9-b138-7331861d6691 · outbound

This paper cites How many and which training points would need to be removed to ﬂip this prediction?.

How to Achieve Higher Accuracy with Less Training Points? How many and which training points would need to be removed to ﬂip this prediction?

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.826076Z

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.

Observation 54365429-f156-437c-abe0-f576f0f4e12f · outbound

This paper cites Relabeling minimal training subset to ﬂip a prediction,.

How to Achieve Higher Accuracy with Less Training Points? Relabeling minimal training subset to ﬂip a prediction,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.815741Z

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.

Observation 1bd74221-ece7-4439-a833-402dcffd6974 · outbound

This paper cites The inﬂuence curve and its role in ro- bust estimation,.

How to Achieve Higher Accuracy with Less Training Points? The inﬂuence curve and its role in ro- bust estimation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.805199Z

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.

Observation 299fb4ad-de52-4cd7-9ada-b95a01d7d1b7 · outbound

This paper cites Characterizations of an empirical inﬂuence function for detecting inﬂuential cases in regression,.

How to Achieve Higher Accuracy with Less Training Points? Characterizations of an empirical inﬂuence function for detecting inﬂuential cases in regression,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.795222Z

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.

Observation eed6243c-2cc3-4641-91a1-2df66f22f3f4 · outbound

This paper cites Machine Unlearning of Features and Labels.

How to Achieve Higher Accuracy with Less Training Points? Machine Unlearning of Features and Labels

Reference 19

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Source-reported events for the cited work

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Observation 5e3c6b1b-baf1-4a43-b52e-b13d9a03caa8 · outbound

This paper cites Resolving training biases via inﬂuence-based data relabeling,.

How to Achieve Higher Accuracy with Less Training Points? Resolving training biases via inﬂuence-based data relabeling,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.784495Z

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.

Observation c23739ec-d022-4be0-a038-d41f1fc6d41b · outbound

This paper cites Datamodels: Understanding predictions with data and data with predictions,.

How to Achieve Higher Accuracy with Less Training Points? Datamodels: Understanding predictions with data and data with predictions,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.772895Z

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.

Observation 6e48b955-a865-4cb8-b73c-cbc1b8b1c9ef · outbound

This paper cites Cardinality-Minimal Explanations for Monotonic Neural Networks.

How to Achieve Higher Accuracy with Less Training Points? Cardinality-Minimal Explanations for Monotonic Neural Networks

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-16T12:07:58.705693Z

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.

Observation 5d060ea3-66e5-4a43-94c2-cec3bf7bda19 · outbound

This paper cites Learning the Difference that Makes a Difference with Counterfactually-Augmented Data.

How to Achieve Higher Accuracy with Less Training Points? Learning the Difference that Makes a Difference with Counterfactually-Augmented Data

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T12:07:58.660399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0b24f80-2beb-450d-ab55-3f3d53338f17 · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment tree- bank,.

How to Achieve Higher Accuracy with Less Training Points? Recursive deep models for semantic compositionality over a sentiment tree- bank,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:58.761383Z

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 7b486d5d-cee4-4b60-9633-64c8496873a8 · inbound

Tri-Info: Generalizable, Interpretable Failure Prediction for VLA Models via Information Theory cites this paper.

Tri-Info: Generalizable, Interpretable Failure Prediction for VLA Models via Information Theory How to Achieve Higher Accuracy with Less Training Points?

Reference 247

Resolution: verified exact; arxiv_id, observed 2026-07-04T04:39:34.369852Z

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.