Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:05:27.565899Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2507.11895.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:05:27.565899Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-21T17:47:21.976868Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T17:50:26.377614Z
36 of 36 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 9839b0e5-13ef-4808-8efe-487590dc9555 · outbound
Newfluence: Boosting Model interpretability and Understanding in High Dimensions write newline
Reference 1
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Approximate leave-one-out cross validation for regression with l1 regularizers
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Reference 3
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Influence Functions in Deep Learning Are Fragile
Reference 4
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Observation 75f96fc4-8231-4d94-b6a1-3d8029dd2be4 · outbound
Newfluence: Boosting Model interpretability and Understanding in High Dimensions and Montanari, A
Reference 5
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Reference 6
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions J., Lim, C., and Yu, B
Reference 7
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions and Zou, J
Reference 8
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Reference 9
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs
Reference 10
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions and Lowd, D
Reference 11
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Reference 12
Source-reported events for the cited work
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Observation 7c19b59b-329b-43de-9e67-2e97852f6bfc · outbound
Newfluence: Boosting Model interpretability and Understanding in High Dimensions Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions
Reference 13
Source-reported events for the cited work
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions M., Engstrom, L., Leclerc, G., and Madry, A
Reference 14
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Observation dc49b621-61bf-42c3-a521-350bc7493d8b · outbound
Newfluence: Boosting Model interpretability and Understanding in High Dimensions and Maleki, A
Reference 15
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions A., Hynes, N., G \"u rel, N
Reference 16
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Reference 17
Source-reported events for the cited work
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Observation c65d0de4-682d-4887-968a-9909d747e7b4 · outbound
Newfluence: Boosting Model interpretability and Understanding in High Dimensions and Zou, J
Reference 18
Source-reported events for the cited work
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 19
Source-reported events for the cited work
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Minimum $\ell_{1}$-norm interpolators: Precise asymptotics and multiple descent
Reference 20
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Understanding Impact of Human Feedback via Influence Functions
Reference 21
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions M., Georgiev, K., Ilyas, A., Leclerc, G., and Madry, A
Reference 22
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Estimating training data influence by tracing gradient descent
Reference 23
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions and Maleki, A
Reference 24
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions
Reference 25
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions The shapley value in machine learning
Reference 26
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Theoretical and practical perspectives on what influence functions do
Reference 27
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Observation 99608b86-5519-4170-b1f6-ab38e858af92 · outbound
Newfluence: Boosting Model interpretability and Understanding in High Dimensions and Najmi, A
Reference 28
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Unresolved cited work
Reference 29
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Data Shapley in One Training Run
Reference 30
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Capturing the Temporal Dependence of Training Data Influence
Reference 31
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Approximate leave-one-out for fast parameter tuning in high dimensions
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions I., and Ravikumar, P
Reference 33
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Correcting large language model behavior via influence function
Reference 34
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Reference 35
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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Certified Data Removal Under High-dimensional Settings
Reference 36
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Reference 25
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