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Paper Citation Record · LEDGER

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data

As of 14 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2502.01634.

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

pith.paper-citation-record.v1
2502.01634 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:50:58.751231Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

22 of 22 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2feedd6e-67ba-4c13-9430-339491316e69 · outbound

This paper cites and Yang, J.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data and Yang, J

Reference 3

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Observation 459b2d11-8987-4c4c-b144-50eca9059ffe · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 7

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Observation bd195104-fc5b-4ac6-a45a-92dffcb39781 · outbound

This paper cites Similarly, W2C represents the testing instances that are wrongly predicted during retraining but are correctly predicted after decremental learning.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data Similarly, W2C represents the testing instances that are wrongly predicted during retraining but are correctly predicted after decremental learning

Reference 8

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Observation 4e73fa1f-65e8-4aa1-a6b3-6d95dd614f28 · outbound

This paper cites Research on Gender-related Fingerprint Features.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data Research on Gender-related Fingerprint Features

Reference 14

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Observation b4d840a8-93af-4040-9557-c44a928eb086 · outbound

This paper cites An overview of gradient descent optimization algorithms.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data An overview of gradient descent optimization algorithms

Reference 15

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Observation 43cef25a-1741-4b12-bedb-f4ea2b39657c · outbound

This paper cites Fast Yet Effective Machine Unlearning.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data Fast Yet Effective Machine Unlearning

Reference 17

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Observation bd041f1e-d9bd-4b57-bc61-495d0e8b1f15 · outbound

This paper cites Structure aware incremental learning with personalized imitation weights for recommender sys- tems.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data Structure aware incremental learning with personalized imitation weights for recommender sys- tems

Reference 18

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Source-reported events for the cited work

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Observation d8ed46a2-5bd1-4490-90d5-a6592d6d27be · outbound

This paper cites SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning

Reference 19

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verified exact
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Observation 5d54a46a-7831-4a36-8812-bed71efb60c2 · outbound

This paper cites Table 7: Error rate after every on- line learning step.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data Table 7: Error rate after every on- line learning step

Reference 20

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 3cbb4fcd-234a-4864-a708-73d1168063cc · outbound

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Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data Unresolved cited work

Reference 22

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Observation b12ddae7-2c16-47de-ab35-e3f4ea3bc281 · outbound

This paper cites Multi-Class Unlearning for Image Classification via Weight Filtering.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data Multi-Class Unlearning for Image Classification via Weight Filtering

Reference 2001

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verified exact
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Observation b1c004a5-c4d8-47ab-8dbf-52d33f1e0675 · outbound

This paper cites Mem- bership inference attacks against machine learning mod- els.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data Mem- bership inference attacks against machine learning mod- els

Reference 2005

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verified fuzzy
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Source-reported events for the cited work

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Observation 08cbefa3-84cd-47cb-97ea-d8755d0f4773 · outbound

This paper cites A Survey of Machine Unlearning.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data A Survey of Machine Unlearning

Reference 2007

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Source-reported events for the cited work

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Observation bf8bca4d-c99d-4fd4-8b4b-92258d52c330 · outbound

This paper cites Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey

Reference 2009

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 4e8f93ec-50b2-4907-8971-37d513034c78 · outbound

This paper cites Fast ABC-Boost: A Unified Framework for Selecting the Base Class in Multi-Class Classification.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data Fast ABC-Boost: A Unified Framework for Selecting the Base Class in Multi-Class Classification

Reference 2010

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Source-reported events for the cited work

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Observation eac2f027-aee1-4993-9341-5c291cc98d0b · outbound

This paper cites Incremental support vector learning: Analysis, implementation and applications.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data Incremental support vector learning: Analysis, implementation and applications

Reference 2013

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Source-reported events for the cited work

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Observation e1cbcef6-1858-4e13-a6c5-777fbc6eb17b · outbound

This paper cites Membership inference attacks from first prin- ciples.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data Membership inference attacks from first prin- ciples

Reference 2015

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verified fuzzy
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Source-reported events for the cited work

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Observation 4affdd6b-9f04-406c-9b26-5851c9a81aa5 · outbound

This paper cites From N to N+1: multiclass transfer incremental learning.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data From N to N+1: multiclass transfer incremental learning

Reference 2017

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Observation 9565f500-39a6-4672-8e05-bdb5cf611e31 · outbound

This paper cites A., Tram `er, F., Carlini, N., and Pa- pernot, N.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data A., Tram `er, F., Carlini, N., and Pa- pernot, N

Reference 2019

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Source-reported events for the cited work

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Observation 49f1d81f-94eb-4b03-85da-b742bd2dd242 · outbound

This paper cites Machine Unlearning for Random Forests.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data Machine Unlearning for Random Forests

Reference 2021

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verified exact
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Source-reported events for the cited work

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Observation d415871a-7e3a-4a04-9463-bf5325b8f1d4 · outbound

This paper cites DMin: Scalable Training Data Influence Estimation for Diffusion Models.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data DMin: Scalable Training Data Influence Estimation for Diffusion Models

Reference 2022

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Source-reported events for the cited work

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Observation 17cd8c85-1b3c-4331-96d9-4afc23b9d378 · outbound

This paper cites CatBoost: gradient boosting with categorical features support.

Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data CatBoost: gradient boosting with categorical features support

Reference 2023

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Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.