Paper Citation Record · LEDGER

Capturing the Temporal Dependence of Training Data Influence

As of 17 August 2026, this Paper Citation Record lists 80 of 80 outbound references and 2 inbound Pith citation observations for arXiv:2412.09538.

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

pith.paper-citation-record.v1
2412.09538 v1

Coverage vector

measured 80 of 80 reference resolution

Typed states for the displayed outbound observations.

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measured 82 of 82 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T15:54:42.929433Z

Reference resolution

80 of 80 outbound references displayed

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External citation measurements

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Outbound references

Observation ce3ab029-d7b1-4931-b90a-d3d82960f70a · outbound

This paper cites write newline.

Capturing the Temporal Dependence of Training Data Influence write newline

Reference 1

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This paper cites @esa (Ref.

Capturing the Temporal Dependence of Training Data Influence @esa (Ref

Reference 2

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Capturing the Temporal Dependence of Training Data Influence Unresolved cited work

Reference 3

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Capturing the Temporal Dependence of Training Data Influence Unresolved cited work

Reference 4

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Observation de50f89c-c3d0-4292-a4a6-114ff8904d94 · outbound

This paper cites Fundamentals of Task-Agnostic Data Valuation.

Capturing the Temporal Dependence of Training Data Influence Fundamentals of Task-Agnostic Data Valuation

Reference 5

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Observation 12b06be1-35ec-4755-938a-fa8c66e81964 · outbound

This paper cites If influence functions are the answer, then what is the question? Advances in Neural Information Processing Systems, 35: 0 17953--17967, 2022.

Capturing the Temporal Dependence of Training Data Influence If influence functions are the answer, then what is the question? Advances in Neural Information Processing Systems, 35: 0 17953--17967, 2022

Reference 6

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Observation bd25474d-9c55-43d6-857d-3142b934ff2b · outbound

This paper cites Training Data Attribution via Approximate Unrolled Differentiation.

Capturing the Temporal Dependence of Training Data Influence Training Data Attribution via Approximate Unrolled Differentiation

Reference 7

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Observation 24d4d929-89b4-49d9-8eed-448e0acb28a7 · outbound

This paper cites Relatif: Identifying explanatory training samples via relative influence.

Capturing the Temporal Dependence of Training Data Influence Relatif: Identifying explanatory training samples via relative influence

Reference 8

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Observation 292ebe32-264c-4373-80f8-6e4f6207c391 · outbound

This paper cites Approximate confidence intervals.

Capturing the Temporal Dependence of Training Data Influence Approximate confidence intervals

Reference 9

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Observation 8caf1753-fd78-4fcb-90df-a803603abb16 · outbound

This paper cites Influence Functions in Deep Learning Are Fragile.

Capturing the Temporal Dependence of Training Data Influence Influence Functions in Deep Learning Are Fragile

Reference 10

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This paper cites Stability and generalization.

Capturing the Temporal Dependence of Training Data Influence Stability and generalization

Reference 11

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Observation 54d7db86-63f3-4592-9dfa-ce6aa40dc7ef · outbound

This paper cites Approximating the shapley value using stratified empirical bernstein sampling.

Capturing the Temporal Dependence of Training Data Influence Approximating the shapley value using stratified empirical bernstein sampling

Reference 12

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Observation 0f18ca9e-1d52-4dd8-8db9-dfbfc652073d · outbound

This paper cites Multi-stage influence function.

Capturing the Temporal Dependence of Training Data Influence Multi-stage influence function

Reference 13

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Observation a33f3eb4-e345-4e88-a004-335b5d3e3966 · outbound

This paper cites Hydra: Hypergradient data relevance analysis for interpreting deep neural networks.

Capturing the Temporal Dependence of Training Data Influence Hydra: Hypergradient data relevance analysis for interpreting deep neural networks

Reference 14

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Observation 7b3dade0-2a60-4d5a-8125-203aca7f466c · outbound

This paper cites What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions.

Capturing the Temporal Dependence of Training Data Influence What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions

Reference 15

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Observation 0f7f2ede-c3db-400b-a9aa-220c5091e405 · outbound

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

Capturing the Temporal Dependence of Training Data Influence Characterizations of an empirical influence function for detecting influential cases in regression

Reference 16

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This paper cites Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution.

Capturing the Temporal Dependence of Training Data Influence Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution

Reference 17

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Observation 24c12f60-8acd-49fe-ae6d-633015aca8dd · outbound

This paper cites Computational copyright: Towards a royalty model for ai music generation platforms.

Capturing the Temporal Dependence of Training Data Influence Computational copyright: Towards a royalty model for ai music generation platforms

Reference 18

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Observation 58349b8f-f51e-4d4b-b108-ecefafa0472a · outbound

This paper cites Efficient Ensembles Improve Training Data Attribution.

Capturing the Temporal Dependence of Training Data Influence Efficient Ensembles Improve Training Data Attribution

Reference 19

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Observation 0aafdff6-8888-4503-94e8-ed0ac753fe13 · outbound

This paper cites Understanding forgetting in continual learning with linear regression.

Capturing the Temporal Dependence of Training Data Influence Understanding forgetting in continual learning with linear regression

Reference 20

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Observation 6d3eb95a-77b7-433c-b149-07d11d12f6ca · outbound

This paper cites Calibrating noise to sensitivity in private data analysis.

Capturing the Temporal Dependence of Training Data Influence Calibrating noise to sensitivity in private data analysis

Reference 21

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Observation 274b72ea-5fda-406f-9b8a-e71f426612e0 · outbound

This paper cites Revisiting the fragility of influence functions.

Capturing the Temporal Dependence of Training Data Influence Revisiting the fragility of influence functions

Reference 22

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Observation 67226062-8c20-4b25-a640-5c34aac3de07 · outbound

This paper cites How catastrophic can catastrophic forgetting be in linear regression? In Conference on Learning Theory, pp.\ 4028--4079.

Capturing the Temporal Dependence of Training Data Influence How catastrophic can catastrophic forgetting be in linear regression? In Conference on Learning Theory, pp.\ 4028--4079

Reference 23

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Observation 0d1979b3-1c87-45ed-aef2-b52f71fd425d · outbound

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Capturing the Temporal Dependence of Training Data Influence Doge: Domain reweighting with generalization estimation

Reference 24

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Observation a260fb5c-22b4-40b7-94f4-c8f239736c9b · outbound

This paper cites What neural networks memorize and why: Discovering the long tail via influence estimation.

Capturing the Temporal Dependence of Training Data Influence What neural networks memorize and why: Discovering the long tail via influence estimation

Reference 25

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Capturing the Temporal Dependence of Training Data Influence The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 26

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This paper cites Data shapley: Equitable valuation of data for machine learning.

Capturing the Temporal Dependence of Training Data Influence Data shapley: Equitable valuation of data for machine learning

Reference 27

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Capturing the Temporal Dependence of Training Data Influence A distributional framework for data valuation

Reference 28

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Observation 8740dada-cbad-4b4f-a8c7-f6c96c459d0d · outbound

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Capturing the Temporal Dependence of Training Data Influence Studying Large Language Model Generalization with Influence Functions

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Capturing the Temporal Dependence of Training Data Influence Fastif: Scalable influence functions for efficient model interpretation and debugging

Reference 30

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Capturing the Temporal Dependence of Training Data Influence How to start training: The effect of initialization and architecture

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Capturing the Temporal Dependence of Training Data Influence Data cleansing for models trained with sgd

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Capturing the Temporal Dependence of Training Data Influence Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

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Capturing the Temporal Dependence of Training Data Influence Estimation of the shapley value by ergodic sampling

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Capturing the Temporal Dependence of Training Data Influence Datamodels: Predicting Predictions from Training Data

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Capturing the Temporal Dependence of Training Data Influence Efficient task-specific data valuation for nearest neighbor algorithms

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Capturing the Temporal Dependence of Training Data Influence Towards efficient data valuation based on the shapley value

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Capturing the Temporal Dependence of Training Data Influence Opendataval: a unified benchmark for data valuation

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Capturing the Temporal Dependence of Training Data Influence Lava: Data valuation without pre-specified learning algorithms

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Capturing the Temporal Dependence of Training Data Influence Understanding black-box predictions via influence functions

Reference 40

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Capturing the Temporal Dependence of Training Data Influence Beta shapley: a unified and noise-reduced data valuation framework for machine learning

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Capturing the Temporal Dependence of Training Data Influence Data-oob: Out-of-bag estimate as a simple and efficient data value

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Capturing the Temporal Dependence of Training Data Influence Efficient computation and analysis of distributional shapley values

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Capturing the Temporal Dependence of Training Data Influence Datainf: Efficiently estimating data influence in lora-tuned llms and diffusion models

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Capturing the Temporal Dependence of Training Data Influence Handwritten digit recognition with a back-propagation network

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Capturing the Temporal Dependence of Training Data Influence Faster approximation of probabilistic and distributional values via least squares

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Capturing the Temporal Dependence of Training Data Influence Robust data valuation with weighted banzhaf values

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Capturing the Temporal Dependence of Training Data Influence Measuring the effect of training data on deep learning predictions via randomized experiments

Reference 48

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Capturing the Temporal Dependence of Training Data Influence New insights and perspectives on the natural gradient method

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Capturing the Temporal Dependence of Training Data Influence Pointer Sentinel Mixture Models

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Capturing the Temporal Dependence of Training Data Influence Sampling permutations for shapley value estimation

Reference 51

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Capturing the Temporal Dependence of Training Data Influence A bayesian approach to analysing training data attribution in deep learning

Reference 52

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Observation e99915ef-a4c7-44e2-80cd-e1017859defc · outbound

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Capturing the Temporal Dependence of Training Data Influence Data valuation without training of a model

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Capturing the Temporal Dependence of Training Data Influence A multilinear sampling algorithm to estimate shapley values

Reference 54

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Observation bba97477-656d-46d4-89c5-6e5ae60adcdd · outbound

This paper cites Trak: attributing model behavior at scale.

Capturing the Temporal Dependence of Training Data Influence Trak: attributing model behavior at scale

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:02:36.290919Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6e7e4e3d-5167-484f-899f-aaab0c973ce6 · outbound

This paper cites Estimating training data influence by tracing gradient descent.

Capturing the Temporal Dependence of Training Data Influence Estimating training data influence by tracing gradient descent

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T17:02:33.932182Z

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Unavailable: canonical work link unavailable.

Observation 43497e55-4fe1-47d3-a693-9e9322a3e469 · outbound

This paper cites Scaling up influence functions.

Capturing the Temporal Dependence of Training Data Influence Scaling up influence functions

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T17:02:33.938848Z

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Observation affc62e8-76e7-494f-bb59-9a224346adf3 · outbound

This paper cites Fast curvature matrix-vector products for second-order gradient descent.

Capturing the Temporal Dependence of Training Data Influence Fast curvature matrix-vector products for second-order gradient descent

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:02:36.245798Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2f4bacdd-8914-4910-a432-53abe830d681 · outbound

This paper cites A value for n-person games.

Capturing the Temporal Dependence of Training Data Influence A value for n-person games

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T17:02:33.949825Z

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Observation a83f18e5-c07a-47a0-b9b3-a96953b16706 · outbound

This paper cites ingredients.

Capturing the Temporal Dependence of Training Data Influence ingredients

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:02:36.215379Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b2444f5a-b4d5-4535-91ca-d732ac9d907c · outbound

This paper cites Revisiting Methods for Finding Influential Examples.

Capturing the Temporal Dependence of Training Data Influence Revisiting Methods for Finding Influential Examples

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T17:02:33.960505Z

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Observation 79a2fad3-a5db-4ef8-953b-b774cd2035de · outbound

This paper cites Incentivizing collaboration in machine learning via synthetic data rewards.

Capturing the Temporal Dependence of Training Data Influence Incentivizing collaboration in machine learning via synthetic data rewards

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:02:36.198231Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3514d3a3-3d3a-4780-a6f8-128bd51695af · outbound

This paper cites Private Data Valuation and Fair Payment in Data Marketplaces.

Capturing the Temporal Dependence of Training Data Influence Private Data Valuation and Fair Payment in Data Marketplaces

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-11T17:02:33.971852Z

Source-reported events for the cited work

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Observation 93aa6859-2e17-416a-9221-e568296de246 · outbound

This paper cites Attention is all you need.

Capturing the Temporal Dependence of Training Data Influence Attention is all you need

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T17:02:33.977857Z

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Unavailable: canonical work link unavailable.

Observation e565032f-331f-492d-85ad-96c6fa3befc4 · outbound

This paper cites Data banzhaf: A robust data valuation framework for machine learning.

Capturing the Temporal Dependence of Training Data Influence Data banzhaf: A robust data valuation framework for machine learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:02:36.162482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 81ac8f2e-42b8-4c15-b1e5-9b75ca52f129 · outbound

This paper cites A Note on "Towards Efficient Data Valuation Based on the Shapley Value''.

Capturing the Temporal Dependence of Training Data Influence A Note on "Towards Efficient Data Valuation Based on the Shapley Value''

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:02:34.311402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 823b2e3a-ba76-4d48-a52d-b90b1f63fddd · outbound

This paper cites A Note on "Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms".

Capturing the Temporal Dependence of Training Data Influence A Note on "Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms"

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-11T17:02:33.998338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 039990cf-8fc8-4bdc-a017-be001edec789 · outbound

This paper cites Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation.

Capturing the Temporal Dependence of Training Data Influence Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T17:02:34.006242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 185ff823-6afc-44c6-86c2-48a69426c24b · outbound

This paper cites An economic solution to copyright challenges of generative ai.

Capturing the Temporal Dependence of Training Data Influence An economic solution to copyright challenges of generative ai

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:02:36.140470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 98958ca7-0980-47a3-ae04-b71b9143db52 · outbound

This paper cites Efficient Data Shapley for Weighted Nearest Neighbor Algorithms.

Capturing the Temporal Dependence of Training Data Influence Efficient Data Shapley for Weighted Nearest Neighbor Algorithms

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:02:34.248991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 089b9bdc-8a2d-44c1-93db-263f3c1149d2 · outbound

This paper cites Data Shapley in One Training Run.

Capturing the Temporal Dependence of Training Data Influence Data Shapley in One Training Run

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-11T17:02:34.024393Z

Source-reported events for the cited work

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Observation 701716ac-8042-4240-bfae-378e05b7ae7e · outbound

This paper cites The power and limitation of pretraining-finetuning for linear regression under covariate shift.

Capturing the Temporal Dependence of Training Data Influence The power and limitation of pretraining-finetuning for linear regression under covariate shift

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:02:36.124545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 735a94dc-d5b0-43fc-8d5b-795f471d9a7a · outbound

This paper cites How many pretraining tasks are needed for in-context learning of linear regression? In The Twelfth International Conference on Learning Representations, 2024.

Capturing the Temporal Dependence of Training Data Influence How many pretraining tasks are needed for in-context learning of linear regression? In The Twelfth International Conference on Learning Representations, 2024

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:02:36.106092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a0f59057-85d3-4c77-9cf1-eb30d5274046 · outbound

This paper cites Davinz: Data valuation using deep neural networks at initialization.

Capturing the Temporal Dependence of Training Data Influence Davinz: Data valuation using deep neural networks at initialization

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:02:36.087434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e947e5cd-f788-42ce-8bf0-4d6c10a52cbb · outbound

This paper cites Validation free and replication robust volume-based data valuation.

Capturing the Temporal Dependence of Training Data Influence Validation free and replication robust volume-based data valuation

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:02:36.070375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 61695a08-1f73-4c7e-9d45-4b6290b2350f · outbound

This paper cites GMValuator: Similarity-based Data Valuation for Generative Models.

Capturing the Temporal Dependence of Training Data Influence GMValuator: Similarity-based Data Valuation for Generative Models

Reference 76

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:02:34.187635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation daedb04d-df64-4e63-85fc-3f6cae207852 · outbound

This paper cites On the Inflation of KNN-Shapley Value.

Capturing the Temporal Dependence of Training Data Influence On the Inflation of KNN-Shapley Value

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:02:34.160376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f7fa9270-c96d-4aa3-9b7c-d09c10d2bb5d · outbound

This paper cites TimeInf: Time Series Data Contribution via Influence Functions.

Capturing the Temporal Dependence of Training Data Influence TimeInf: Time Series Data Contribution via Influence Functions

Reference 78

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:02:34.133195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b41bede9-1596-40a1-9359-74689657d539 · outbound

This paper cites The elements of statistical learning, 2003.

Capturing the Temporal Dependence of Training Data Influence The elements of statistical learning, 2003

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:02:34.769473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5830e102-aa66-4317-b26d-867e0140b4e2 · outbound

This paper cites Benign overfitting of constant-stepsize sgd for linear regression.

Capturing the Temporal Dependence of Training Data Influence Benign overfitting of constant-stepsize sgd for linear regression

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:02:34.750948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation 8b6cddc4-3d21-4dae-a9fa-9c7ded7fd880 · inbound

Newfluence: Boosting Model interpretability and Understanding in High Dimensions cites this paper.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Capturing the Temporal Dependence of Training Data Influence

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:27.084783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ae4da80-2c05-45fd-a668-02abaf49840a · inbound

Better Training Data Attribution via Better Inverse Hessian-Vector Products cites this paper.

Better Training Data Attribution via Better Inverse Hessian-Vector Products Capturing the Temporal Dependence of Training Data Influence

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:54:42.932940Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.