Paper Citation Record · LEDGER

InterpretML: A Unified Framework for Machine Learning Interpretability

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 33 inbound Pith citation observations for arXiv:1909.09223.

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

pith.paper-citation-record.v1
1909.09223 v1

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 33 of 33 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:13:27.964089Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

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External citation measurements

322
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2b31b821-87ac-4c58-b558-b84314e6d770 · inbound

Capabilities of GPT-4 on Medical Challenge Problems cites this paper.

Capabilities of GPT-4 on Medical Challenge Problems InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 17

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A Human-Centric Approach to Explainable AI for Personalized Education cites this paper.

A Human-Centric Approach to Explainable AI for Personalized Education InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 2023

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CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions cites this paper.

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 36

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ExplainBench: A Benchmark Framework for Local Model Explanations in Fairness-Critical Applications cites this paper.

ExplainBench: A Benchmark Framework for Local Model Explanations in Fairness-Critical Applications InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 5

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midr: Learning from Black-Box Models by Maximum Interpretation Decomposition cites this paper.

midr: Learning from Black-Box Models by Maximum Interpretation Decomposition InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 35

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Observation 2cb681b4-9f82-46d7-b705-bc02c83fee69 · inbound

TabArena: A Living Benchmark for Machine Learning on Tabular Data cites this paper.

TabArena: A Living Benchmark for Machine Learning on Tabular Data InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 18

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Observation 60b611a7-851a-4822-b7bf-5a2d25f042a0 · inbound

Unveiling Location-Specific Price Drivers: A Two-Stage Cluster Analysis for Interpretable House Price Predictions cites this paper.

Unveiling Location-Specific Price Drivers: A Two-Stage Cluster Analysis for Interpretable House Price Predictions InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 1

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Observation 350648d9-4d3d-41dd-9313-d0268d7d2e98 · inbound

Long-Term Variability in Physiological-Arousal Relationships for Robust Emotion Estimation cites this paper.

Long-Term Variability in Physiological-Arousal Relationships for Robust Emotion Estimation InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 40

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Transparent and Fair Profiling in Employment Services: Evidence from Switzerland cites this paper.

Transparent and Fair Profiling in Employment Services: Evidence from Switzerland InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 10

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Cluster-Based Generalized Additive Models Informed by Random Fourier Features cites this paper.

Cluster-Based Generalized Additive Models Informed by Random Fourier Features InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 25

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Interpretable, Physics-Informed Learning Reveals Sulfur Adsorption and Poisoning Mechanisms in 13-Atom Icosahedra Nanoclusters cites this paper.

Interpretable, Physics-Informed Learning Reveals Sulfur Adsorption and Poisoning Mechanisms in 13-Atom Icosahedra Nanoclusters InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 71

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Selecting Feature Interactions for Generalized Additive Models by Distilling Foundation Models cites this paper.

Selecting Feature Interactions for Generalized Additive Models by Distilling Foundation Models InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 15

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Observation 84ac1b97-758c-419d-9544-c48412fb1aa6 · inbound

Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures cites this paper.

Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 5

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Observation 769b4c12-2b53-4d22-b336-a53ce5af423e · inbound

ParamBoost: Gradient Boosted Piecewise Cubic Polynomials cites this paper.

ParamBoost: Gradient Boosted Piecewise Cubic Polynomials InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 18

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Observation 142de909-d246-476f-9646-b31c643ab7de · inbound

FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment cites this paper.

FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 46

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Observation b9d1ff5d-6ec0-4a4b-8d1d-318d67494721 · inbound

FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment cites this paper.

FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 43

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Gradient Boosted Risk Scores cites this paper.

Gradient Boosted Risk Scores InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 13

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Observation ab2d9a1d-fba5-4bab-8135-f0231b307dbc · inbound

Agentic-imodels: Evolving agentic interpretability tools via autoresearch cites this paper.

Agentic-imodels: Evolving agentic interpretability tools via autoresearch InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 13

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A Foundation Model for Zero-Shot Logical Rule Induction cites this paper.

A Foundation Model for Zero-Shot Logical Rule Induction InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 21

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TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models cites this paper.

TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 21

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Observation 85c0b6c6-a468-4ae2-abaa-3156f58eac98 · inbound

TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models cites this paper.

TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 22

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Empirical estimates of how massive galaxies can be in {\Lambda}CDM cites this paper.

Empirical estimates of how massive galaxies can be in {\Lambda}CDM InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 180

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Empirical estimates of how massive galaxies can be in {\Lambda}CDM cites this paper.

Empirical estimates of how massive galaxies can be in {\Lambda}CDM InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 180

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Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology cites this paper.

Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 22

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Generalized Functional ANOVA in Closed-Form: A Unified View of Additive Explanations cites this paper.

Generalized Functional ANOVA in Closed-Form: A Unified View of Additive Explanations InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 41

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Observation 5c9dd2bf-aa11-4aec-a7f4-9a10df967328 · inbound

Can machine learning for quantum-gas experiments be explainable? cites this paper.

Can machine learning for quantum-gas experiments be explainable? InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 27

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DEM: A Distilled Explanation Model for Interpretable Anomaly Detection in Physiological Sensor Networks cites this paper.

DEM: A Distilled Explanation Model for Interpretable Anomaly Detection in Physiological Sensor Networks InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 25

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FlagGAM: Rule-Basis Generalized Additive Models for Explainable Tabular Prediction cites this paper.

FlagGAM: Rule-Basis Generalized Additive Models for Explainable Tabular Prediction InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 8

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Revisiting urban heat indices in Switzerland using low-cost measurement networks cites this paper.

Revisiting urban heat indices in Switzerland using low-cost measurement networks InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 50

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Neural Additive and Basis Models with Feature Selection and Interactions cites this paper.

Neural Additive and Basis Models with Feature Selection and Interactions InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 16

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The Quadrilateral Loss: Additivity as a Measurable Behavior of Dense Neural Networks cites this paper.

The Quadrilateral Loss: Additivity as a Measurable Behavior of Dense Neural Networks InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 22

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Economic Complexity as a Determinant of Regional Human Development in Brazil: Evidence across Aggregation Scales cites this paper.

Economic Complexity as a Determinant of Regional Human Development in Brazil: Evidence across Aggregation Scales InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 18

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From population norms to personalized trajectories: interpretable Bayesian forecasting for cognitive decline cites this paper.

From population norms to personalized trajectories: interpretable Bayesian forecasting for cognitive decline InterpretML: A Unified Framework for Machine Learning Interpretability

Reference 45

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