Paper Citation Record · LEDGER

Evaluating Data Influence in Meta Learning

As of 17 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 2 inbound Pith citation observations for arXiv:2501.15963.

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

pith.paper-citation-record.v1
2501.15963 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:54:50.348998Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:02:09.747575Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T20:39:26.673080Z

Reference resolution

12 of 12 outbound references displayed

verified exact2
verified fuzzy4
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1f12757d-00e4-4e61-800e-6e33b8ba26e5 · outbound

This paper cites Fastif: Scalable influence functions for efficient model interpretation and debugging.

Evaluating Data Influence in Meta Learning Fastif: Scalable influence functions for efficient model interpretation and debugging

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:54:50.546614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f090d5b3-e69f-4bb4-9c7a-8cf897805b2e · outbound

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

Evaluating Data Influence in Meta Learning Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T13:54:50.316907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a745c4d-1e80-4eb7-b197-5b1045852644 · outbound

This paper cites Meta-SGD: Learning to Learn Quickly for Few-Shot Learning.

Evaluating Data Influence in Meta Learning Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T13:54:50.326764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cff8b216-b57a-4fe2-bcd0-eafa181024d9 · outbound

This paper cites Adaptive task sampling for meta-learning.

Evaluating Data Influence in Meta Learning Adaptive task sampling for meta-learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:54:50.533305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation aba3a363-d3be-420d-b93d-d64f565d6c14 · outbound

This paper cites Meta-Learning with Latent Embedding Optimization.

Evaluating Data Influence in Meta Learning Meta-Learning with Latent Embedding Optimization

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T13:54:50.339522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fa1d56e-3dd4-40f2-b6b4-17a833c84923 · outbound

This paper cites Learning to continuously optimize wireless resource in a dynamic environment: A bilevel optimization perspective.

Evaluating Data Influence in Meta Learning Learning to continuously optimize wireless resource in a dynamic environment: A bilevel optimization perspective

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:54:50.519185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3078e100-66c8-4a32-a6ce-9c5a64658fa5 · outbound

This paper cites 17 Evaluating Data Influence in Meta Learning A PREPRINT where Hλ,Total = P i∈I DλDλLO λ∗, θi(λ∗); Dval i.

Evaluating Data Influence in Meta Learning 17 Evaluating Data Influence in Meta Learning A PREPRINT where Hλ,Total = P i∈I DλDλLO λ∗, θi(λ∗); Dval i

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:54:50.502128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3a3ea66b-09ca-481b-b03e-49dd17f2f6fc · outbound

This paper cites Robust Ante-hoc Graph Explainer using Bilevel Optimization.

Evaluating Data Influence in Meta Learning Robust Ante-hoc Graph Explainer using Bilevel Optimization

Reference 2017

Resolution: verified exact; local_arxiv, observed 2026-08-10T13:54:50.435021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 75512fe4-8559-4d51-9fc4-b8690f144544 · outbound

This paper cites Meta-Learning with Warped Gradient Descent.

Evaluating Data Influence in Meta Learning Meta-Learning with Warped Gradient Descent

Reference 2018

Resolution: verified exact; local_arxiv, observed 2026-08-10T13:54:50.477329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ba5b8a1c-338d-43f9-9d50-29a222fe0eb3 · outbound

This paper cites Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm.

Evaluating Data Influence in Meta Learning Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T13:54:50.294764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2da585dd-3551-4d5b-afea-838981f79648 · outbound

This paper cites Recasting Gradient-Based Meta-Learning as Hierarchical Bayes.

Evaluating Data Influence in Meta Learning Recasting Gradient-Based Meta-Learning as Hierarchical Bayes

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T13:54:50.305285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b3a03f2-69dc-4397-9a29-8fbf004e5499 · outbound

This paper cites On First-Order Meta-Learning Algorithms.

Evaluating Data Influence in Meta Learning On First-Order Meta-Learning Algorithms

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-10T13:54:50.335249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation e2915214-8635-4745-a2d2-22d64b7f6400 · inbound

Attributing Data for Sharpness-Aware Minimization cites this paper.

Attributing Data for Sharpness-Aware Minimization Evaluating Data Influence in Meta Learning

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T20:02:09.747575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97385bba-1145-4265-a1f9-a43d12c021a4 · inbound

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning cites this paper.

From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning Evaluating Data Influence in Meta Learning

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:39:26.676425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.