Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data | Paper Citation Record

Observation 39b61e03-47f8-4de9-86a5-0e8845c39299 · outbound

This paper cites Tldr: Deep learning-based automated privacy policy annotation with key policy highlights.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Tldr: Deep learning-based automated privacy policy annotation with key policy highlights

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.314857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56d51929-c7ee-4a61-996c-22ea313481bf · outbound

This paper cites A convergence theory for deep learning via over-parameterization.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data A convergence theory for deep learning via over-parameterization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.323551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2163eca3-49af-4d34-bb52-d6b183076a0c · outbound

This paper cites Transductive versions of the lasso and the dantzig selector.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Transductive versions of the lasso and the dantzig selector

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.333393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2f7f5db-8200-43ad-9664-95a7220f2ec9 · outbound

This paper cites Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.345108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0168d43d-f40f-444b-b63b-9dbdc8f79068 · outbound

This paper cites Bartlett, Dylan J.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Bartlett, Dylan J

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.351844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c77697e-de8a-4e71-a8c6-51e7c6a5262f · outbound

This paper cites Benign overfitting in linear regression.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Benign overfitting in linear regression

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.358278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7658ce33-182d-4a17-8bdd-0c4263a70f23 · outbound

This paper cites Hsu, and Partha Mitra.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Hsu, and Partha Mitra

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.378108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8386505-3e15-4357-b32b-041471e713ae · outbound

This paper cites Reconciling modern machine-learning practice and the classical bias–variance trade-off.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Reconciling modern machine-learning practice and the classical bias–variance trade-off

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.386003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 554bbcea-71fe-4c68-ba87-90e84b35de8e · outbound

This paper cites Generalized inverses: theory and appli- cations, volume 15.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Generalized inverses: theory and appli- cations, volume 15

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.396948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 156d661c-785a-4e92-a5ed-b8e5669b550e · outbound

This paper cites Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networks.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.407882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9c16595-a17b-453a-865b-6be841a8aff5 · outbound

This paper cites A new look at an old problem: A univer- sal learning approach to linear regression.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data A new look at an old problem: A univer- sal learning approach to linear regression

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.418129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 742e2ecf-b593-4b0f-a8a2-98adcff69863 · outbound

This paper cites Learning rotation invariant features for cryogenic electron microscopy image re- construction.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Learning rotation invariant features for cryogenic electron microscopy image re- construction

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.424629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 242a3975-5ecb-4327-9ae5-f62127d3c801 · outbound

This paper cites Evasion attacks against machine learning at test time.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Evasion attacks against machine learning at test time

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.433463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 987c833b-7521-4152-902a-4466047f4b1c · outbound

This paper cites The description length of deep learning models.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data The description length of deep learning models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.441637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab133727-9fd0-44dd-bc1b-ab1dd9df5147 · outbound

This paper cites On Evaluating Adversarial Robustness.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data On Evaluating Adversarial Robustness

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.453328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac89abbc-cd83-47b5-a2a8-bbabed2edaab · outbound

This paper cites Unlabeled data improves adversarial robustness.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Unlabeled data improves adversarial robustness

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.485112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb8d82f4-2ec9-4bf0-b571-261f2d581b6a · outbound

This paper cites Transductive inference for estimating values of functions.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Transductive inference for estimating values of functions

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.498932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b2449c1-2fe6-4125-9a3d-775db7f972ea · outbound

This paper cites Hopskipjumpattack: A query-efficient decision-based attack.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Hopskipjumpattack: A query-efficient decision-based attack

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.516316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cbc9f41-1d76-47f0-b7c8-b18680927f2b · outbound

This paper cites Emnist: Extending mnist to handwritten letters.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Emnist: Extending mnist to handwritten letters

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.523605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47f8fc69-0c38-4cac-8a47-df6197f4681e · outbound

This paper cites On transductive regression.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data On transductive regression

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.548586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86853eb9-0ebe-4c5f-8d67-cde98c418ead · outbound

This paper cites Laplace redux-effortless bayesian deep learn- ing.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Laplace redux-effortless bayesian deep learn- ing

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.584646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5956927f-18f3-4f99-8d6d-8ae23153eaa5 · outbound

This paper cites Mathematics for machine learning, chapter 9.3.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Mathematics for machine learning, chapter 9.3

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.604750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 432c7931-c30f-490a-9688-83386a4250d8 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Imagenet: A large-scale hierarchical image database

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.634752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abea52cf-f1e6-4e01-8917-15143335549e · outbound

This paper cites The mnist database of handwritten digit images for machine learning research [best of the web].IEEE signal processing magazine, 29(6):141–142, 2012.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data The mnist database of handwritten digit images for machine learning research [best of the web].IEEE signal processing magazine, 29(6):141–142, 2012

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.664751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c431e89-b338-4df2-9f69-9b892a7b8cdb · outbound

This paper cites Reducing network agnostophobia.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Reducing network agnostophobia

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.680069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09a50b94-04f5-4a88-b2b6-ac6012dfe17a · outbound

This paper cites UCI machine learning repository, 2017.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data UCI machine learning repository, 2017

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.716059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff57d101-ac47-401e-ab79-61bbdf5f2e48 · outbound

This paper cites Revisiting minimum description length complexity in overparameterized models.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Revisiting minimum description length complexity in overparameterized models

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-11T18:53:11.904752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bb4202dd-e40c-4564-b343-938c6914ed5e · outbound

This paper cites Wainwright.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Wainwright

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.749155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4138b0c-f57a-4025-b62c-4c9762d7d94d · outbound

This paper cites Theory of optimal experiments.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Theory of optimal experiments

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.759275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4397ec8e-2cb6-4ded-b10e-5435b1121be7 · outbound

This paper cites The use of multiple measurements in taxonomic problems.Annals of eugenics, 7(2):179–188, 1936.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data The use of multiple measurements in taxonomic problems.Annals of eugenics, 7(2):179–188, 1936

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.768183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01996fab-1f3c-4db6-89fc-d24b0012d096 · outbound

This paper cites On the problem of on-line learning with log-loss.IEEE International Symposium on Information Theory - Proceedings, pages 2995–2999,.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data On the problem of on-line learning with log-loss.IEEE International Symposium on Information Theory - Proceedings, pages 2995–2999,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.787532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da68c0e3-31f7-41b6-a11e-fc1257dcff0a · outbound

This paper cites Universal batch learning with log-loss.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Universal batch learning with log-loss

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.815942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc8f18b4-85f1-42e2-9999-d848c0167c43 · outbound

This paper cites Universal learning of individual data.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Universal learning of individual data

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.826706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e5cca97-2736-4273-9ed9-e1f44508d6c9 · outbound

This paper cites Dropout as a bayesian approximation: Repre- senting model uncertainty in deep learning.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Dropout as a bayesian approximation: Repre- senting model uncertainty in deep learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.840447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dba6246-04a4-4b9d-a8ea-14674d7b1fae · outbound

This paper cites Deep bayesian active learning with image data.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Deep bayesian active learning with image data

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.852522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a480eb4b-976c-40e2-92c8-e3f33efea6df · outbound

This paper cites Degrees of freedom in deep neural networks.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Degrees of freedom in deep neural networks

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.859985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51bd8b8d-ece3-4883-aba6-a2a0156f0271 · outbound

This paper cites Deep learning.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Deep learning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.869022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64f6698b-e507-4326-91ed-24f4041429ab · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Explaining and Harnessing Adversarial Examples

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.879547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f66fae0-c4c9-4c8b-85cb-d156c0512dd8 · outbound

This paper cites Machine learning for social science: An agnostic approach.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Machine learning for social science: An agnostic approach

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.888131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d750e82-98c8-4080-9ca7-08d747213ff9 · outbound

This paper cites The minimum description length principle.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data The minimum description length principle

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.899228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a5038df-51d5-4de9-a612-8111cff29575 · outbound

This paper cites Lee, Daniel Soudry, and Nati Srebro.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Lee, Daniel Soudry, and Nati Srebro

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.914186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53a75aea-608a-4a44-82fa-3b374aa52dc2 · outbound

This paper cites Coun- tering adversarial images using input transformations.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Coun- tering adversarial images using input transformations

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.927781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0b714ee-b3fc-45fa-ba8d-746bbe415f90 · outbound

This paper cites Friedman.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Friedman

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.937562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac4dc5cd-0d4d-4ac1-9a89-0ca169f4ec6f · outbound

This paper cites Surprises in High-Dimensional Ridgeless Least Squares Interpolation.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Surprises in High-Dimensional Ridgeless Least Squares Interpolation

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.945673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6e8c9c4-3cc7-4213-8e9d-1713be1973ff · outbound

This paper cites 9.4: Recursive least squares.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data 9.4: Recursive least squares

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.956777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76b5b47d-1026-4df3-a176-608515a3bb3a · outbound

This paper cites Deep residual learning for image recognition.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Deep residual learning for image recognition

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.971261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e892211-2bf0-4b0b-85c6-9ddc3873d0b0 · outbound

This paper cites A baseline for detecting misclassified and out- of-distribution examples in neural networks.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data A baseline for detecting misclassified and out- of-distribution examples in neural networks

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.984266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 187fb769-4a7b-4fbb-a013-370e59e7a68f · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:09.993571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 139eba2f-8743-4042-a8e2-8104d9cc9bde · outbound

This paper cites Using self- supervised learning can improve model robustness and uncertainty.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Using self- supervised learning can improve model robustness and uncertainty

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.007408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2406deba-5a40-4409-a9e5-5ba238c6131c · outbound

This paper cites Probabilistic backpropagation for scalable learning of bayesian neural networks.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Probabilistic backpropagation for scalable learning of bayesian neural networks

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.021547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42fbbccd-2119-424c-979b-876f422905dc · outbound

This paper cites Efficient computation of normalized maximum like- lihood coding for gaussian mixtures with its applications to optimal clustering.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Efficient computation of normalized maximum like- lihood coding for gaussian mixtures with its applications to optimal clustering

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.038019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 768a8355-f3c9-48fd-9795-4cf3a913ff6b · outbound

This paper cites Hoerl and R Kennard.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Hoerl and R Kennard

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.047879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd34d54a-d044-4598-8ecf-3cfe94222695 · outbound

This paper cites Bayesian Active Learning for Classification and Preference Learning.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Bayesian Active Learning for Classification and Preference Learning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.069384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9ec9086-3267-4838-8098-e0a30d96a286 · outbound

This paper cites Densely connected convolutional networks.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Densely connected convolutional networks

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.093629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54b61527-45e9-4e27-9529-7fb83456252e · outbound

This paper cites DeepAL: Deep Active Learning in Python.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data DeepAL: Deep Active Learning in Python

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.110693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8f44783-9e38-4a5e-8123-6d03bc82308e · outbound

This paper cites An introduc- tion to statistical learning, volume 112.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data An introduc- tion to statistical learning, volume 112

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.120661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6f4d126-07cb-41cd-97be-e31722313c27 · outbound

This paper cites Fantastic generalization measures and where to find them.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Fantastic generalization measures and where to find them

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.127176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d72bbd94-c720-4da2-a5e3-52191da9c437 · outbound

This paper cites On the complexity of linear prediction: Risk bounds, margin bounds, and regularization.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data On the complexity of linear prediction: Risk bounds, margin bounds, and regularization

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.134247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 837a9b83-b4d4-4ce7-839c-278a4cd43e94 · outbound

This paper cites Balancing specialization, generalization, and compression for detection and track- ing.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Balancing specialization, generalization, and compression for detection and track- ing

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.147304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36ebc282-2a41-462d-b6a7-5283686c2930 · outbound

This paper cites The cifar-10 dataset.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data The cifar-10 dataset

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:16.995971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e7e0585e-6737-447d-b0be-a4fe7489ffcb · outbound

This paper cites Solving least squares problems , vol- ume 15.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Solving least squares problems , vol- ume 15

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:16.934743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 28854fb5-9731-4404-97ee-dd79f0b70ced · outbound

This paper cites MNIST handwritten digit database.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data MNIST handwritten digit database

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:16.883721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e0883a7e-06aa-49aa-bcc6-b538ae81752c · outbound

This paper cites A simple unified framework for detecting out-of-distribution samples and adversarial attacks.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data A simple unified framework for detecting out-of-distribution samples and adversarial attacks

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:16.814762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 99d05c64-280c-463f-931d-c3c1256853b0 · outbound

This paper cites Near-optimal linear regression under distribution shift.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Near-optimal linear regression under distribution shift

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:16.702162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d1f82d82-5f45-45ff-842c-961b57023279 · outbound

This paper cites Measuring the intrinsic dimension of objective landscapes.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Measuring the intrinsic dimension of objective landscapes

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:16.614832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6bdb3df7-2e68-40cc-a93c-d896a695a353 · outbound

This paper cites an unresolved cited work.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-11T18:53:16.514289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4fbf62a1-a14c-46d3-b5e2-8f3d59bfc686 · outbound

This paper cites Just interpolate: Kernel ridgeless re- gression can generalize.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Just interpolate: Kernel ridgeless re- gression can generalize

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:16.435698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a9a30069-bb44-40da-b77e-8af5a0adb82b · outbound

This paper cites Ridge regression: Structure, cross-validation, and sketching.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Ridge regression: Structure, cross-validation, and sketching

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:16.333934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 930fc5df-87b7-46f2-b465-99d38d5e39d4 · outbound

This paper cites Energy-based out-of- distribution detection.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Energy-based out-of- distribution detection

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:16.244750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 648cfe01-a00a-4b15-953f-a2cc0d001f6b · outbound

This paper cites Deep learning face at- tributes in the wild.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Deep learning face at- tributes in the wild

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:16.153142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8334df0e-6608-4cce-9ef8-b9d608e3b7d8 · outbound

This paper cites The Generalization Error of the Minimum-norm Solutions for Over-parameterized Neural Networks.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data The Generalization Error of the Minimum-norm Solutions for Over-parameterized Neural Networks

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.312485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60eaa49c-cc02-42e1-b708-18c60eb2a359 · outbound

This paper cites Information-based objective functions for active data selection.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Information-based objective functions for active data selection

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:16.088010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f0d90f6b-10c8-4bad-a197-776cc5c947d4 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.329846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18c605f6-657a-4540-8dbc-e430b6869812 · outbound

This paper cites Universal prediction.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Universal prediction

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:16.005314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 989dbfa4-6484-42b9-8d3b-8033a0b98adb · outbound

This paper cites Normalized Maximum Likelihood with Luckiness for Multivariate Normal Distributions.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Normalized Maximum Likelihood with Luckiness for Multivariate Normal Distributions

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-08-11T18:53:11.344258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c63ee255-2856-47ba-9bc5-e8a124209cde · outbound

This paper cites Harmless interpolation of noisy data in regression.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Harmless interpolation of noisy data in regression

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:15.911221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2f6ef4e8-bd5f-4d7c-ae12-595303d66d7a · outbound

This paper cites Optimal Regularization Can Mitigate Double Descent.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Optimal Regularization Can Mitigate Double Descent

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.365287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fbea74d-cfb1-431c-ae2b-9e3a08811021 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Reading digits in natural images with unsupervised feature learning

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:15.771923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1b4b99c7-809c-42de-a93c-cc5ffbb1936e · outbound

This paper cites A pac-bayesian approach to spectrally-normalized margin bounds for neural networks.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data A pac-bayesian approach to spectrally-normalized margin bounds for neural networks

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:15.638083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7b5df05b-5fe7-41b7-89b8-2bef2763038c · outbound

This paper cites Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-08-11T18:53:11.250377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bcd1c617-fccd-4893-925a-ba8f920db91b · outbound

This paper cites Olson, William La Cava, Patryk Orzechowski, Ryan J.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Olson, William La Cava, Patryk Orzechowski, Ryan J

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.415984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2e80e63-19e4-40f0-8a02-8a584eb35b66 · outbound

This paper cites Outlier exposure with confidence control for out-of-distribution detection.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Outlier exposure with confidence control for out-of-distribution detection

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:15.519345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a5e7621c-0bb4-48bd-a1d1-5f1a82d67d35 · outbound

This paper cites Practical black-box attacks against machine learning.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Practical black-box attacks against machine learning

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:15.341100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bb7db2a6-b2c9-48fb-967d-9d05cecbfae4 · outbound

This paper cites Evaluation methods in face recognition.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Evaluation methods in face recognition

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:15.245535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 26429507-18f8-4d62-8975-d793e5e3c0c1 · outbound

This paper cites Ad- versarial robustness through local linearization.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Ad- versarial robustness through local linearization

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:15.110221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2e8632da-596e-4872-abce-e2935a3a1ebc · outbound

This paper cites Information-based complexity, feedback and dynamics in convex programming.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Information-based complexity, feedback and dynamics in convex programming

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:15.046776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 577978c8-aa93-4422-bfef-6210ca65b524 · outbound

This paper cites A survey of deep active learning.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data A survey of deep active learning

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.465470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad11f546-782b-41c9-bdc5-6e31f583cef2 · outbound

This paper cites Mdl regression and denoising.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Mdl regression and denoising

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:14.888139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 71631046-eb9c-41d0-9540-620a2ebce052 · outbound

This paper cites Defense-GAN: Protect- ing classifiers against adversarial attacks using generative models.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Defense-GAN: Protect- ing classifiers against adversarial attacks using generative models

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:14.760936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f4a7c16e-32d1-4f6d-88f4-3b06cb28af6b · outbound

This paper cites Detecting out-of-distribution exam- ples with Gram matrices.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Detecting out-of-distribution exam- ples with Gram matrices

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:14.682715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a7de1edf-c25c-43bf-a31d-c7063ce7e3a4 · outbound

This paper cites Toward open set recognition.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Toward open set recognition

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:14.584752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 74379126-262c-45e5-9b8b-2c28d51b924d · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-11T18:53:10.506001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2dfc783d-bc45-4d74-8e0e-72385aef6f4d · outbound

This paper cites On the asymptotic distribution of ridge re- gression estimators using training and test samples.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data On the asymptotic distribution of ridge re- gression estimators using training and test samples

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:14.453357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 93a04cc2-3dc7-4aac-b858-6bb5734fb404 · outbound

This paper cites Convolutional neural net- works applied to house numbers digit classification.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Convolutional neural net- works applied to house numbers digit classification

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:14.294892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 15b44e18-6225-4899-a7df-2e0bfba9da8d · outbound

This paper cites Minimum norm solutions do not always generalize well for over-parameterized problems.stat, 1050:16, 2018.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Minimum norm solutions do not always generalize well for over-parameterized problems.stat, 1050:16, 2018

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:14.144850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c3b95e30-e300-4907-9a40-5f3d04b4f5f9 · outbound

This paper cites Learn- ability, stability and uniform convergence.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Learn- ability, stability and uniform convergence

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:13.984755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 635da1eb-b0a0-4ac4-b0b4-b03df967c276 · outbound

This paper cites The sample complexity of learning linear predictors with the squared loss.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data The sample complexity of learning linear predictors with the squared loss

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:13.825024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 60ec9ca7-6adc-4e2d-974e-124d221bd150 · outbound

This paper cites Universal active learning via conditional mutual information minimization.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Universal active learning via conditional mutual information minimization

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:13.694760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0f5260ed-70be-473c-a182-3ee00d670ec4 · outbound

This paper cites Minimax active learning via minimal model capacity.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Minimax active learning via minimal model capacity

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:13.568883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3bee3eb7-771f-4681-8b0b-e405d09b0c34 · outbound

This paper cites Universal sequential coding of single messages.Prob- lemy Peredachi Informatsii, 23(3):3–17, 1987.

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data Universal sequential coding of single messages.Prob- lemy Peredachi Informatsii, 23(3):3–17, 1987

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:53:13.501123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers