Reliable and scalable variable importance estimation via warm-start and early stopping | Paper Citation Record

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This paper cites Why Are We Using Black Box Models in AI When We Don t Need To ? A Lesson From an Explainable AI Competition.

Reliable and scalable variable importance estimation via warm-start and early stopping Why Are We Using Black Box Models in AI When We Don t Need To ? A Lesson From an Explainable AI Competition

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Reliable and scalable variable importance estimation via warm-start and early stopping A survey of methods for explaining black box models

Reference 2

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This paper cites Allen, Luqin Gan, and Lili Zheng.

Reliable and scalable variable importance estimation via warm-start and early stopping Allen, Luqin Gan, and Lili Zheng

Reference 3

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Reliable and scalable variable importance estimation via warm-start and early stopping Unresolved cited work

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Reliable and scalable variable importance estimation via warm-start and early stopping Unresolved cited work

Reference 5

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Reliable and scalable variable importance estimation via warm-start and early stopping Unresolved cited work

Reference 6

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This paper cites Learning important features through propagating activation differences.

Reliable and scalable variable importance estimation via warm-start and early stopping Learning important features through propagating activation differences

Reference 7

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Reliable and scalable variable importance estimation via warm-start and early stopping Axiomatic attribution for deep networks

Reference 8

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This paper cites Learning important features through propagating activation differences.

Reliable and scalable variable importance estimation via warm-start and early stopping Learning important features through propagating activation differences

Reference 9

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Reliable and scalable variable importance estimation via warm-start and early stopping Axiomatic attribution for deep networks

Reference 10

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This paper cites Nonparametric variable importance using an augmented neural network with multi-task learning.

Reliable and scalable variable importance estimation via warm-start and early stopping Nonparametric variable importance using an augmented neural network with multi-task learning

Reference 11

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Reliable and scalable variable importance estimation via warm-start and early stopping Dropout feature ranking for deep learning models, 2018

Reference 12

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Reliable and scalable variable importance estimation via warm-start and early stopping Morgan and H

Reference 13

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This paper cites Wainwright, and Bin Yu.

Reliable and scalable variable importance estimation via warm-start and early stopping Wainwright, and Bin Yu

Reference 14

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Reliable and scalable variable importance estimation via warm-start and early stopping Boosting with the l2 loss

Reference 15

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Reliable and scalable variable importance estimation via warm-start and early stopping Boosting with early stopping: Convergence and consistency

Reference 16

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Reliable and scalable variable importance estimation via warm-start and early stopping Wainwright1

Reference 17

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Reliable and scalable variable importance estimation via warm-start and early stopping Lazy Estimation of VI for Large NNs

Reference 18

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Reliable and scalable variable importance estimation via warm-start and early stopping Unresolved cited work

Reference 19

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This paper cites Theory and methods related to the singular-function expansion and landweber's iteration for integral equations of the first kind.

Reliable and scalable variable importance estimation via warm-start and early stopping Theory and methods related to the singular-function expansion and landweber's iteration for integral equations of the first kind

Reference 20

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This paper cites Process consistency for adaboost.

Reliable and scalable variable importance estimation via warm-start and early stopping Process consistency for adaboost

Reference 21

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Reliable and scalable variable importance estimation via warm-start and early stopping On early stopping in gradient descent learning

Reference 22

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Reliable and scalable variable importance estimation via warm-start and early stopping Boosting algorithms: Regularization, prediction and model fitting

Reference 23

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Reliable and scalable variable importance estimation via warm-start and early stopping Unresolved cited work

Reference 24

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Reliable and scalable variable importance estimation via warm-start and early stopping Neural tangent kernel: Convergence and generalization in neural networks

Reference 25

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This paper cites Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, and Ruosong Wang.

Reliable and scalable variable importance estimation via warm-start and early stopping Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, and Ruosong Wang

Reference 26

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Reliable and scalable variable importance estimation via warm-start and early stopping A convergence theory for deep learning via over-parameterization

Reference 27

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This paper cites Du, Jason D.

Reliable and scalable variable importance estimation via warm-start and early stopping Du, Jason D

Reference 28

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This paper cites Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh.

Reliable and scalable variable importance estimation via warm-start and early stopping Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh

Reference 29

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This paper cites Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington.

Reliable and scalable variable importance estimation via warm-start and early stopping Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington

Reference 30

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Reliable and scalable variable importance estimation via warm-start and early stopping Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 31

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Reliable and scalable variable importance estimation via warm-start and early stopping Gradient boosting performs gaussian process inference

Reference 32

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Reliable and scalable variable importance estimation via warm-start and early stopping Relative importance for linear regression in r: The package relaimpo

Reference 33

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Reliable and scalable variable importance estimation via warm-start and early stopping Nathans, Frederick L

Reference 34

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Reliable and scalable variable importance estimation via warm-start and early stopping Unresolved cited work

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Reliable and scalable variable importance estimation via warm-start and early stopping Williamson, Peter B

Reference 36

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Reliable and scalable variable importance estimation via warm-start and early stopping Williamson, Peter B

Reference 37

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Reliable and scalable variable importance estimation via warm-start and early stopping Variable importance in binary regression trees and forests

Reference 38

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Reliable and scalable variable importance estimation via warm-start and early stopping A time-dependent energy-momentum method

Reference 39

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This paper cites Bias in random forest variable importance measures: Illustrations, sources and a solution.

Reliable and scalable variable importance estimation via warm-start and early stopping Bias in random forest variable importance measures: Illustrations, sources and a solution

Reference 40

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Reliable and scalable variable importance estimation via warm-start and early stopping On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation

Reference 41

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Reliable and scalable variable importance estimation via warm-start and early stopping Unresolved cited work

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Reliable and scalable variable importance estimation via warm-start and early stopping Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems

Reference 43

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This paper cites A Unified Approach to Interpreting Model Predictions.

Reliable and scalable variable importance estimation via warm-start and early stopping A Unified Approach to Interpreting Model Predictions

Reference 44

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Reliable and scalable variable importance estimation via warm-start and early stopping Understanding global feature contributions with additive importance measures

Reference 45

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Reliable and scalable variable importance estimation via warm-start and early stopping Williamson and Jean Feng

Reference 46

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This paper cites Lundberg, Hugh Chen Gabriel Erion, Alex DeGrave, Jordan M.

Reliable and scalable variable importance estimation via warm-start and early stopping Lundberg, Hugh Chen Gabriel Erion, Alex DeGrave, Jordan M

Reference 47

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Reliable and scalable variable importance estimation via warm-start and early stopping Covert, Scott M

Reference 48

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Reliable and scalable variable importance estimation via warm-start and early stopping Unresolved cited work

Reference 49

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Reliable and scalable variable importance estimation via warm-start and early stopping Unresolved cited work

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Reliable and scalable variable importance estimation via warm-start and early stopping Covert, Scott M

Reference 51

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This paper cites Floodgate: inference for model-free variable importance.

Reliable and scalable variable importance estimation via warm-start and early stopping Floodgate: inference for model-free variable importance

Reference 52

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This paper cites All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously.

Reliable and scalable variable importance estimation via warm-start and early stopping All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously

Reference 53

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This paper cites Model class reliance for random forests.

Reliable and scalable variable importance estimation via warm-start and early stopping Model class reliance for random forests

Reference 54

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This paper cites Tibshirani, and Larry Wasserman.

Reliable and scalable variable importance estimation via warm-start and early stopping Tibshirani, and Larry Wasserman

Reference 55

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This paper cites Bootstrapping and sample splitting for high-dimensional, assumption-lean inference.

Reliable and scalable variable importance estimation via warm-start and early stopping Bootstrapping and sample splitting for high-dimensional, assumption-lean inference

Reference 56

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Observation 606bfd84-b496-4467-9f7a-cd080a188af4 · outbound

This paper cites The hardness of conditional independence testing and the generalised covariance measure.

Reliable and scalable variable importance estimation via warm-start and early stopping The hardness of conditional independence testing and the generalised covariance measure

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.745895Z

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Observation 6d51e66c-2f4e-4daa-867f-64f4221ebea8 · outbound

This paper cites On Azadkia–Chatterjee’s conditional dependence coefficient.

Reliable and scalable variable importance estimation via warm-start and early stopping On Azadkia–Chatterjee’s conditional dependence coefficient

Reference 58

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Observation 0e8f6911-9052-44cd-b8ec-7f3ecf5762f1 · outbound

This paper cites Testing conditional independence in supervised learning algorithms.

Reliable and scalable variable importance estimation via warm-start and early stopping Testing conditional independence in supervised learning algorithms

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.727146Z

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Observation 512f146b-c979-4c4c-848b-67d116d22618 · outbound

This paper cites Panning for gold: ‘model-x’ knockoffs for high dimensional controlled variable selection.

Reliable and scalable variable importance estimation via warm-start and early stopping Panning for gold: ‘model-x’ knockoffs for high dimensional controlled variable selection

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T04:47:44.709245Z

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Observation 9790801a-be75-444e-bcc9-884a021c99f0 · outbound

This paper cites Functions of positive and negative type and their connection with the theory of integral equations.

Reliable and scalable variable importance estimation via warm-start and early stopping Functions of positive and negative type and their connection with the theory of integral equations

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.709884Z

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Observation 7a7641a0-fabc-4940-a337-69152c929215 · outbound

This paper cites Random matrix approximation of spectra of integral operators.

Reliable and scalable variable importance estimation via warm-start and early stopping Random matrix approximation of spectra of integral operators

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.691551Z

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Observation e7157af8-b799-4e07-80f0-5fcb3fcc0db9 · outbound

This paper cites Owen and Cl\' e mentine Prieur.

Reliable and scalable variable importance estimation via warm-start and early stopping Owen and Cl\' e mentine Prieur

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T04:47:44.724772Z

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Observation 85668feb-280c-42b3-9115-117485c84bb2 · outbound

This paper cites Interpretable Machine Learning.

Reliable and scalable variable importance estimation via warm-start and early stopping Interpretable Machine Learning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.674484Z

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Observation 876a206b-2af1-4337-b6f5-0ed2dc15eb21 · outbound

This paper cites Mendelson.

Reliable and scalable variable importance estimation via warm-start and early stopping Mendelson

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.657079Z

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Observation 757a6915-d0a4-430e-888e-af6b8bb075bd · outbound

This paper cites Progressive growing of gans for improved quality, stability, and variation.

Reliable and scalable variable importance estimation via warm-start and early stopping Progressive growing of gans for improved quality, stability, and variation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.638472Z

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Observation b94e6dd5-bf3d-49ca-b7f1-0ec0b51d63c0 · outbound

This paper cites Park, Jascha Sohl-Dickstein, Quoc V.

Reliable and scalable variable importance estimation via warm-start and early stopping Park, Jascha Sohl-Dickstein, Quoc V

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.621061Z

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

Observation bbf09c92-6f8f-4079-b48c-bcca17f6c11f · outbound

This paper cites Gradient descent finds global minima of deep neural networks.

Reliable and scalable variable importance estimation via warm-start and early stopping Gradient descent finds global minima of deep neural networks

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.603733Z

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Observation 81f5cb3a-f882-4e8b-a046-7d60eff0e2e7 · outbound

This paper cites On the inductive bias of neural tangent kernels.

Reliable and scalable variable importance estimation via warm-start and early stopping On the inductive bias of neural tangent kernels

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.586125Z

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

Observation ecefec25-ea00-4a51-b3c2-cbf92c693a0d · outbound

This paper cites Breaking the curse of dimensionality with convex neural networks.

Reliable and scalable variable importance estimation via warm-start and early stopping Breaking the curse of dimensionality with convex neural networks

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.568517Z

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

Observation 5fc5295d-1ed7-451e-926c-0a596cd35ac5 · outbound

This paper cites Deep neural tangent kernel and laplace kernel have the same rkhs.

Reliable and scalable variable importance estimation via warm-start and early stopping Deep neural tangent kernel and laplace kernel have the same rkhs

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.549164Z

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

Observation 86f184e8-6b1e-46e7-a2e3-31c75db063f3 · outbound

This paper cites On the similarity between the laplace and neural tangent kernels.

Reliable and scalable variable importance estimation via warm-start and early stopping On the similarity between the laplace and neural tangent kernels

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.532611Z

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Observation 2c7078c2-9957-4c32-b58a-37466fedd32a · outbound

This paper cites Deep equals shallow for relu networks in kernel regimes.

Reliable and scalable variable importance estimation via warm-start and early stopping Deep equals shallow for relu networks in kernel regimes

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.515266Z

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

Observation 09a70c7f-e1ef-4453-97d3-b6c0c00c2267 · outbound

This paper cites On the approximate realization of continuous mappings by neural networks.

Reliable and scalable variable importance estimation via warm-start and early stopping On the approximate realization of continuous mappings by neural networks

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-12T04:47:44.782330Z

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Observation fa93cf07-314a-41d3-9b66-4d1e4c7ebede · outbound

This paper cites Approximation capabilities of multilayer feedforward networks.

Reliable and scalable variable importance estimation via warm-start and early stopping Approximation capabilities of multilayer feedforward networks

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-12T04:47:44.787368Z

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Observation aa5cf58e-a0f7-4923-b646-8fb666aae90a · outbound

This paper cites Friedman.

Reliable and scalable variable importance estimation via warm-start and early stopping Friedman

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-12T04:47:44.792610Z

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Observation 22587c8f-804e-434a-88c2-3e2e358aad06 · outbound

This paper cites Gas Turbine CO and NOx Emission Data Set.

Reliable and scalable variable importance estimation via warm-start and early stopping Gas Turbine CO and NOx Emission Data Set

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-12T04:47:44.797591Z

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Observation d3a31ec7-9ebc-41bc-a27e-ccbccb5c79a1 · outbound

This paper cites Data analysis and symbolic regression models for predicting co and nox emissions from gas turbines.

Reliable and scalable variable importance estimation via warm-start and early stopping Data analysis and symbolic regression models for predicting co and nox emissions from gas turbines

Reference 78

Resolution: verified exact; doi, observed 2026-08-12T04:47:44.875905Z

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Observation c70ac35f-e3b4-41ef-a8b9-b763cb5e4311 · outbound

This paper cites Abdul Karim Miah, and Md Azazul Haque.

Reliable and scalable variable importance estimation via warm-start and early stopping Abdul Karim Miah, and Md Azazul Haque

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-12T04:47:44.807691Z

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Observation 34be1fff-cdc0-4796-b681-dd1f84d01cc5 · outbound

This paper cites Pytorch lightning, 2019.

Reliable and scalable variable importance estimation via warm-start and early stopping Pytorch lightning, 2019

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.498270Z

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

Observation f3a548ca-d6ca-4230-87b8-a36e642c9d3c · outbound

This paper cites Catboost: gradient boosting with categorical features support, 2018.

Reliable and scalable variable importance estimation via warm-start and early stopping Catboost: gradient boosting with categorical features support, 2018

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:47:45.481281Z

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