Paper Citation Record · LEDGER

Cost-Effective Retraining of Machine Learning Models

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.04216.

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

pith.paper-citation-record.v1
2310.04216 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:31:06.755713Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T13:13:27.372167Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0639c339-dbf4-4917-8d95-a0ee5f102a4e · inbound

Provable unlearning in topic modeling and downstream tasks cites this paper.

Provable unlearning in topic modeling and downstream tasks Cost-Effective Retraining of Machine Learning Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T17:31:06.755713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53a43470-696b-49f3-8737-078940e73216 · inbound

Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift cites this paper.

Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift Cost-Effective Retraining of Machine Learning Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T16:43:20.124767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3de3646-64e5-475e-b2e4-d15a8b640bcf · inbound

Intelligent Elastic Feature Fading: Enabling Model Retrain-Free Feature Efficiency Rollouts at Scale cites this paper.

Intelligent Elastic Feature Fading: Enabling Model Retrain-Free Feature Efficiency Rollouts at Scale Cost-Effective Retraining of Machine Learning Models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:41:52.548361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 08d625b2-4d80-4cdd-89a0-b7719bfd1cc7 · inbound

TIMEGATE: Sustainable Time-Boxed Promotion Gates for Continual ML Adaptation Under Resource Constraints cites this paper.

TIMEGATE: Sustainable Time-Boxed Promotion Gates for Continual ML Adaptation Under Resource Constraints Cost-Effective Retraining of Machine Learning Models

Reference 2

Resolution: malformed identifier; arxiv_id, observed 2026-06-29T13:13:27.373785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 43d22fc9-086f-4fad-9a53-28e9a839dd8f · inbound

LYRA: Label-Free Structural Synchronization and Resource Allocation for UAV Edge Networks cites this paper.

LYRA: Label-Free Structural Synchronization and Resource Allocation for UAV Edge Networks Cost-Effective Retraining of Machine Learning Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T05:20:17.914520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbd3aa9e-20f5-4a8c-bc77-4773eff55582 · inbound

Lightweight PID-Based Drift Mitigation for Cellular Traffic Forecasting cites this paper.

Lightweight PID-Based Drift Mitigation for Cellular Traffic Forecasting Cost-Effective Retraining of Machine Learning Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T00:12:08.329158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.