Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation | Paper Citation Record

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This paper cites Universality in learning from linear measurements.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Universality in learning from linear measurements

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation A Novel Gaussian Min-Max Theorem and its Applications

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Regularized linear regression for binary classification

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Multiple fourier series and fourier integrals

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Wasserstein Distributionally Robust Estimation in High Dimensions: Performance Analysis and Optimal Hyperparameter Tuning

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This paper cites Limit theorems for distributions invariant under groups of transformations.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Limit theorems for distributions invariant under groups of transformations

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Regularized estimation in sparse high-dimensional time series models

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

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Reconciling modern machine-learning practice and the classical bias–variance trade-off

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This paper cites Learning invariances in neural networks from training data.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Learning invariances in neural networks from training data

Reference 9

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This paper cites Sur l'extension du th \'e or \`e me limite du calcul des probabilit \'e s aux sommes de quantit \'e s d \'e pendantes.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Sur l'extension du th \'e or \`e me limite du calcul des probabilit \'e s aux sommes de quantit \'e s d \'e pendantes

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Probability and measure

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Logistic regression for dependent binary observations

Reference 12

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Basic properties of strong mixing conditions

Reference 13

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This paper cites Simple technical trading rules and the stochastic properties of stock returns.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Simple technical trading rules and the stochastic properties of stock returns

Reference 14

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Distributional and lq norm inequalities for polynomials over convex bodies in rn

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Concentration inequalities with exchangeable pairs

Reference 16

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This paper cites A group-theoretic framework for data augmentation.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation A group-theoretic framework for data augmentation

Reference 17

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This paper cites Universality of approximate message passing algorithms.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Universality of approximate message passing algorithms

Reference 18

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This paper cites Statistics for spatial data.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Statistics for spatial data

Reference 19

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This paper cites Time series analysis, volume 286.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Time series analysis, volume 286

Reference 20

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This paper cites Universality laws for G aussian mixtures in generalized linear models.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Universality laws for G aussian mixtures in generalized linear models

Reference 21

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This paper cites A central limit theorem for globally nonstationary near-epoch dependent functions of mixing processes.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation A central limit theorem for globally nonstationary near-epoch dependent functions of mixing processes

Reference 22

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This paper cites A model of double descent for high-dimensional binary linear classification.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation A model of double descent for high-dimensional binary linear classification

Reference 23

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This paper cites A note on empirical processes of strong-mixing sequences.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation A note on empirical processes of strong-mixing sequences

Reference 24

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This paper cites On the inherent regularization effects of noise injection during training.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation On the inherent regularization effects of noise injection during training

Reference 25

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This paper cites Message-passing algorithms for compressed sensing.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Message-passing algorithms for compressed sensing

Reference 26

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This paper cites Lu, and Subhabrata Sen.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Lu, and Subhabrata Sen

Reference 27

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This paper cites Handbook of spatial statistics.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Handbook of spatial statistics

Reference 28

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Gaussian universality of perceptrons with random labels

Reference 29

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Some inequalities for G aussian processes and applications

Reference 30

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation High Dimensional and Banded Vector Autoregressions

Reference 31

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Universality of regularized regression estimators in high dimensions

Reference 32

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Data augmentation as stochastic optimization

Reference 33

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This paper cites Analysis of dichotomous response data from certain toxicological experiments.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Analysis of dichotomous response data from certain toxicological experiments

Reference 34

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This paper cites Surprises in high-dimensional ridgeless least squares interpolation.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Surprises in high-dimensional ridgeless least squares interpolation

Reference 35

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This paper cites Universality laws for high-dimensional learning with random features.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Universality laws for high-dimensional learning with random features

Reference 36

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Data augmentation in the underparameterized and overparameterized regimes

Reference 37

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation A high-dimensional convergence theorem for u-statistics with applications to kernel-based testing

Reference 38

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Independent and stationary sequences of random variables

Reference 39

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This paper cites The theory of approximation, volume 11.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation The theory of approximation, volume 11

Reference 40

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This paper cites Precise statistical analysis of classification accuracies for adversarial training.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Precise statistical analysis of classification accuracies for adversarial training

Reference 41

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This paper cites Asymptotic behavior of unregularized and ridge-regularized high-dimensional robust regression estimators : rigorous results, 2013.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Asymptotic behavior of unregularized and ridge-regularized high-dimensional robust regression estimators : rigorous results, 2013

Reference 42

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This paper cites Label-imbalanced and group-sensitive classification under overparameterization.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Label-imbalanced and group-sensitive classification under overparameterization

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.692907Z

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Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Applications of the lindeberg principle in communications and statistical learning

Reference 44

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Observation d83f31a0-268c-4e51-88d7-d26e9e74258c · outbound

This paper cites Universality in block dependent linear models with applications to nonparametric regression.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Universality in block dependent linear models with applications to nonparametric regression

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.663083Z

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Observation bf9ded2a-e032-4238-ac48-6fe89d983581 · outbound

This paper cites Probability in Banach Spaces: isoperimetry and processes.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Probability in Banach Spaces: isoperimetry and processes

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.647465Z

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Observation 713cabd1-154d-43a2-b652-29e0e49bb624 · outbound

This paper cites The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.632868Z

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Observation 5b0bb5e1-37bb-4ccb-ac9a-2e9cfb4ab459 · outbound

This paper cites On the benefits of invariance in neural networks.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation On the benefits of invariance in neural networks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.617859Z

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Observation 0a212e06-92b4-4cc2-aaa0-b9089e6bb309 · outbound

This paper cites The generalization error of random features regression: Precise asymptotics and the double descent curve.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation The generalization error of random features regression: Precise asymptotics and the double descent curve

Reference 49

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Observation 4aefc7a4-64f8-47c7-be58-b0e650fe7244 · outbound

This paper cites The role of regularization in classification of high-dimensional noisy G aussian mixture.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation The role of regularization in classification of high-dimensional noisy G aussian mixture

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.594294Z

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Observation a2075ad3-40b3-4cd5-ac71-9e2b18d8e407 · outbound

This paper cites Universality of the elastic net error.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Universality of the elastic net error

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.579749Z

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Observation 1af252eb-972c-495e-a871-0e677db81978 · outbound

This paper cites Universality of empirical risk minimization.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Universality of empirical risk minimization

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-08T14:37:13.466171Z

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Observation 5e6d5f04-4fcd-452a-809b-05fd50ccb850 · outbound

This paper cites Universality of max-margin classifiers.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Universality of max-margin classifiers

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T14:37:13.470662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5b37fe2-7e7b-4354-a6a0-ff1490d0620d · outbound

This paper cites High Dimensional Logistic Regression Under Network Dependence.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation High Dimensional Logistic Regression Under Network Dependence

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-08T14:37:13.848819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2e5c82dc-3a0f-4f59-8ba0-419796cac167 · outbound

This paper cites Least squares regression with markovian data: Fundamental limits and algorithms.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Least squares regression with markovian data: Fundamental limits and algorithms

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.555911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 026ed7dc-ece0-430b-a339-9c6169e08664 · outbound

This paper cites Nicholson, Ines Wilms, Jacob Bien, and David S.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Nicholson, Ines Wilms, Jacob Bien, and David S

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.541980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2f173875-2ddf-4037-a4a2-00a3178e6e1d · outbound

This paper cites From naive mean field theory to the tap equations.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation From naive mean field theory to the tap equations

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.527986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dcb93acd-54f6-4d06-a310-c1bf6a3b211a · outbound

This paper cites Universality laws for randomized dimension reduction, with applications.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Universality laws for randomized dimension reduction, with applications

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.514469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e16a9352-58a6-457f-985c-7ec28e5a09f6 · outbound

This paper cites an unresolved cited work.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:37:14.500922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 118a6ea9-e8c1-44c4-bc8c-fc469e570ae2 · outbound

This paper cites Correlated binary regression with covariates specific to each binary observation.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Correlated binary regression with covariates specific to each binary observation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.487085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cf824fdb-f586-4cd1-a649-1663a1c78299 · outbound

This paper cites Locally dependent latent class models with covariates: an application to under-age drinking in the usa.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Locally dependent latent class models with covariates: an application to under-age drinking in the usa

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.473742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 12e62e7b-ef47-4ba3-b58b-fb108c788d0e · outbound

This paper cites Linear models in statistics.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Linear models in statistics

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.459972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 069baf87-b0ad-4eea-b30e-cf410786cc51 · outbound

This paper cites Tyrrell Rockafellar.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Tyrrell Rockafellar

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.445982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f805e8f3-22ce-442f-a253-5183238243e1 · outbound

This paper cites Fundamentals of Stein’s method.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Fundamentals of Stein’s method

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.431450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2f4b82c0-fc9c-4835-b1df-7ed88dfaab03 · outbound

This paper cites Hanson-wright inequality and sub-gaussian concentration, 2013.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Hanson-wright inequality and sub-gaussian concentration, 2013

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.416238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fcebb612-797e-4e90-8f8d-27fc4d3f943b · outbound

This paper cites The impact of regularization on high-dimensional logistic regression.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation The impact of regularization on high-dimensional logistic regression

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.401168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fb854ead-1a23-4ec4-b10b-62fdac500cf8 · outbound

This paper cites Local dependence in random graph models: characterization, properties and statistical inference.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Local dependence in random graph models: characterization, properties and statistical inference

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.386608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 03960834-aa5a-4714-8307-19b61022ba02 · outbound

This paper cites Advanced Data Analysis from an Elementary Point of View.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Advanced Data Analysis from an Elementary Point of View

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.371579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 404aefa0-1e2b-4f88-9e83-c4f26d030acb · outbound

This paper cites Shorten and T.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Shorten and T

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.355554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7b0dd421-5e9c-471e-830b-5af6c179d660 · outbound

This paper cites Text data augmentation for deep learning.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Text data augmentation for deep learning

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.341179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d5dddfc6-07ea-4ab9-9f57-e1eee70da46b · outbound

This paper cites A framework to characterize performance of LASSO algorithms.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation A framework to characterize performance of LASSO algorithms

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-08T14:37:13.549753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34a4cd53-7eea-49ec-89fe-6ba30b94328d · outbound

This paper cites Upper-bounding $\ell_1$-optimization weak thresholds.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Upper-bounding $\ell_1$-optimization weak thresholds

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-08T14:37:13.554923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ca2f5a0-9cd6-4555-94e0-5d97d46349df · outbound

This paper cites A modern maximum-likelihood theory for high-dimensional logistic regression.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation A modern maximum-likelihood theory for high-dimensional logistic regression

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.326651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 51c8b5e3-9561-41d6-a3b4-250a7d97e57b · outbound

This paper cites The impact of multi-optimizers and data augmentation on tensorflow convolutional neural network performance.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation The impact of multi-optimizers and data augmentation on tensorflow convolutional neural network performance

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.312647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c79cd2aa-6f56-4fa8-8541-3387defbe0c1 · outbound

This paper cites Recovering structured signals in high dimensions via non-smooth convex optimization: Precise performance analysis.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Recovering structured signals in high dimensions via non-smooth convex optimization: Precise performance analysis

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.298447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 85f64c7a-560d-4e42-b223-3feecd69343b · outbound

This paper cites The Gaussian min-max theorem in the Presence of Convexity.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation The Gaussian min-max theorem in the Presence of Convexity

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-08T14:37:13.573836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cd9d62a-53e2-49da-aadb-014274d996f4 · outbound

This paper cites Regularized linear regression: A precise analysis of the estimation error.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Regularized linear regression: A precise analysis of the estimation error

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-08T14:37:13.578609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecfea4fa-28a7-4fdb-9328-292bacdfa85b · outbound

This paper cites Precise error analysis of regularized m -estimators in high dimensions.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Precise error analysis of regularized m -estimators in high dimensions

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.275048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5ef4f1f9-6b62-4649-83f4-0ab239be924d · outbound

This paper cites Analysis of financial time series.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Analysis of financial time series

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.260107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b777d44f-1375-44e2-83d7-1914b46b554f · outbound

This paper cites Some mixing properties of time series models.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Some mixing properties of time series models

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.245367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 00f8eca7-cf7a-4155-899f-d470eecb4679 · outbound

This paper cites High-Dimensional Probability: An Introduction with Applications in Data Science.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation High-Dimensional Probability: An Introduction with Applications in Data Science

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-08T14:37:13.596660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e54eca60-3ba1-4a11-8d47-167f4bd4fa7d · outbound

This paper cites An overview on data augmentation for machine learning.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation An overview on data augmentation for machine learning

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.221660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c0b4453e-a4e2-4792-bf89-f88fdd34a537 · outbound

This paper cites Multivariate geostatistics: an introduction with applications.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Multivariate geostatistics: an introduction with applications

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.206890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f0bc6e26-c88e-46de-a0a7-eabf1839d4dc · outbound

This paper cites Universality of approximate message passing algorithms and tensor networks.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Universality of approximate message passing algorithms and tensor networks

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.190045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1a07c62d-bb0d-4ffb-9359-f270abf89805 · outbound

This paper cites Regularized estimation in high dimensional time series under mixing conditions.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Regularized estimation in high dimensional time series under mixing conditions

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.175912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 273b0a90-b22c-47fe-a8fc-25640a770a76 · outbound

This paper cites Lasso guarantees for -mixing heavy-tailed time series.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Lasso guarantees for -mixing heavy-tailed time series

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.161677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f9d0593d-da00-4169-87b4-018aeea08c40 · outbound

This paper cites On the use of repeated measurements in regression analysis with dichotomous responses.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation On the use of repeated measurements in regression analysis with dichotomous responses

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.146713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ee68baaa-9779-4659-9bd2-7b6221b7d965 · outbound

This paper cites Generative adversarial symmetry discovery.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Generative adversarial symmetry discovery

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.132139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a6573a08-8e49-43ee-b362-076dcbcdee36 · outbound

This paper cites Rates of convergence for empirical processes of stationary mixing sequences.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Rates of convergence for empirical processes of stationary mixing sequences

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-08T14:37:13.632681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce2e537c-6eb5-4a4d-a1f3-974b9bfcb576 · outbound

This paper cites Learning local dependence in ordered data.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Learning local dependence in ordered data

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.108760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 504e73dc-042e-412c-89fb-47724494c32e · outbound

This paper cites Generalized estimating equation models for correlated data: A review with applications.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation Generalized estimating equation models for correlated data: A review with applications

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.093970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a3109f0c-d0eb-40cd-bd9f-ea7ed1f8d3b0 · outbound

This paper cites The generalization performance of erm algorithm with strongly mixing observations.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation The generalization performance of erm algorithm with strongly mixing observations

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:37:14.079320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Reference resolution

Outbound references

Pith citing papers