Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function | Paper Citation Record

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This paper cites GPT-4 Technical Report.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function GPT-4 Technical Report

Reference 1

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This paper cites Self-improving algorithms.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Self-improving algorithms

Reference 2

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This paper cites Sparse linear networks with a fixed butterfly structure: theory and practice.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Sparse linear networks with a fixed butterfly structure: theory and practice

Reference 3

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This paper cites Neural network learning: Theoretical foundations, volume 9.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Neural network learning: Theoretical foundations, volume 9

Reference 4

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This paper cites Designing neural network architectures using reinforcement learning.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Designing neural network architectures using reinforcement learning

Reference 5

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This paper cites Data-Driven Algorithm Design.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Data-Driven Algorithm Design

Reference 6

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Observation 76a2fe61-6cab-4e2f-a487-b599c5a27c04 · outbound

This paper cites Data driven semi-supervised learning.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Data driven semi-supervised learning

Reference 7

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This paper cites Learning accurate and interpretable decision trees.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Learning accurate and interpretable decision trees

Reference 8

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This paper cites Sample complexity of automated mechanism design.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Sample complexity of automated mechanism design

Reference 9

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Observation ad149221-de6b-4c59-9021-e06e107d9a49 · outbound

This paper cites Learning-theoretic foundations of algorithm configuration for combinatorial partitioning problems.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Learning-theoretic foundations of algorithm configuration for combinatorial partitioning problems

Reference 10

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This paper cites Learning to branch.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Learning to branch

Reference 11

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This paper cites Data-driven clustering via parameterized L loyd's families.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Data-driven clustering via parameterized L loyd's families

Reference 12

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This paper cites A general theory of sample complexity for multi-item profit maximization.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function A general theory of sample complexity for multi-item profit maximization

Reference 13

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This paper cites Learning to link.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Learning to link

Reference 14

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This paper cites Refined bounds for algorithm configuration: The knife-edge of dual class approximability.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Refined bounds for algorithm configuration: The knife-edge of dual class approximability

Reference 15

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This paper cites How much data is sufficient to learn high-performing algorithms? G eneralization guarantees for data-driven algorithm design.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function How much data is sufficient to learn high-performing algorithms? G eneralization guarantees for data-driven algorithm design

Reference 16

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This paper cites Sample complexity of tree search configuration: Cutting planes and beyond.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Sample complexity of tree search configuration: Cutting planes and beyond

Reference 17

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This paper cites Provably tuning the ElasticNet across instances.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Provably tuning the ElasticNet across instances

Reference 18

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This paper cites Structural analysis of branch-and-cut and the learnability of G omory mixed integer cuts.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Structural analysis of branch-and-cut and the learnability of G omory mixed integer cuts

Reference 19

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This paper cites New bounds for hyperparameter tuning of regression problems across instances.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function New bounds for hyperparameter tuning of regression problems across instances

Reference 20

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This paper cites Algorithm Configuration for Structured Pfaffian Settings.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Algorithm Configuration for Structured Pfaffian Settings

Reference 21

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This paper cites Almost linear VC dimension bounds for piecewise polynomial networks.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Almost linear VC dimension bounds for piecewise polynomial networks

Reference 22

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This paper cites Generalization bounds for data-driven numerical linear algebra.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Generalization bounds for data-driven numerical linear algebra

Reference 23

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This paper cites Spectrally-normalized margin bounds for neural networks.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Spectrally-normalized margin bounds for neural networks

Reference 24

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This paper cites Nearly-tight VC -dimension and pseudodimension bounds for piecewise linear neural networks.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Nearly-tight VC -dimension and pseudodimension bounds for piecewise linear neural networks

Reference 25

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Observation 78a22b4c-197c-4789-8ca5-f8886ad4c677 · outbound

This paper cites Random search for hyper-parameter optimization.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Random search for hyper-parameter optimization

Reference 26

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This paper cites Algorithms for hyper-parameter optimization.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Algorithms for hyper-parameter optimization

Reference 27

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This paper cites Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures

Reference 28

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This paper cites Learning from labeled and unlabeled data using graph mincuts.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Learning from labeled and unlabeled data using graph mincuts

Reference 29

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Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Advanced calculus

Reference 30

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 31

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This paper cites Nas-bench-201: Extending the scope of reproducible neural architecture search.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Nas-bench-201: Extending the scope of reproducible neural architecture search

Reference 32

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This paper cites Simple and efficient architecture search for CNN s.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Simple and efficient architecture search for CNN s

Reference 33

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This paper cites Neural architecture search: A survey.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Neural architecture search: A survey

Reference 34

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This paper cites Neural message passing for quantum chemistry.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Neural message passing for quantum chemistry

Reference 35

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This paper cites A PAC approach to application-specific algorithm selection.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function A PAC approach to application-specific algorithm selection

Reference 36

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Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Data-driven algorithm design

Reference 37

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This paper cites Hyperparameter optimization: A spectral approach.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Hyperparameter optimization: A spectral approach

Reference 38

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This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 39

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This paper cites Sequential model-based optimization for general algorithm configuration.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Sequential model-based optimization for general algorithm configuration

Reference 40

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This paper cites Learning-based low-rank approximations.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Learning-based low-rank approximations

Reference 41

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This paper cites Polynomial bounds for VC dimension of sigmoidal and general P faffian neural networks.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Polynomial bounds for VC dimension of sigmoidal and general P faffian neural networks

Reference 42

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Observation cfb885b6-12b8-49e3-b7b2-deb4e22cd047 · outbound

This paper cites Learning to relax: Setting solver parameters across a sequence of linear system instances.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Learning to relax: Setting solver parameters across a sequence of linear system instances

Reference 43

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This paper cites Fewnomials, volume 88.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Fewnomials, volume 88

Reference 44

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This paper cites Kipf and Max Welling.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Kipf and Max Welling

Reference 45

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This paper cites Geometry-aware gradient algorithms for neural architecture search.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Geometry-aware gradient algorithms for neural architecture search

Reference 46

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This paper cites Hyperband: A novel bandit-based approach to hyperparameter optimization.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Hyperband: A novel bandit-based approach to hyperparameter optimization

Reference 47

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This paper cites Learning the positions in countsketch.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Learning the positions in countsketch

Reference 48

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Observation 29f03b06-f32f-4c1b-af52-11b8e24a8adc · outbound

This paper cites Progressive neural architecture search.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Progressive neural architecture search

Reference 49

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This paper cites DARTS : Differentiable architecture search.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function DARTS : Differentiable architecture search

Reference 50

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Observation 055fbc13-1631-4179-9690-ba567394026d · outbound

This paper cites Learning algebraic multigrid using graph neural networks.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Learning algebraic multigrid using graph neural networks

Reference 51

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Observation 9c413f68-278a-400e-b2ce-890720611674 · outbound

This paper cites Neural nets with superlinear VC -dimension.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Neural nets with superlinear VC -dimension

Reference 52

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Observation 0b548c92-8ed0-4b01-a4d6-c08204e8d01f · outbound

This paper cites NAS-Bench-Suite: NAS evaluation is (now) surprisingly easy.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function NAS-Bench-Suite: NAS evaluation is (now) surprisingly easy

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.237591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9a4bce2c-7e46-46a7-a721-85a540e7ba49 · outbound

This paper cites Towards automatically-tuned neural networks.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Towards automatically-tuned neural networks

Reference 54

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Observation 1df5c196-c275-464c-99cf-b40db4c45c44 · outbound

This paper cites DeepArchitect: Automatically Designing and Training Deep Architectures.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function DeepArchitect: Automatically Designing and Training Deep Architectures

Reference 55

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Observation 156ce9da-784f-4901-844c-a3b352477a21 · outbound

This paper cites Efficient neural architecture search via parameters sharing.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Efficient neural architecture search via parameters sharing

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.208482Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation efd91783-721a-4c2b-b4f3-083e6f8a2eeb · outbound

This paper cites Convergence of stochastic processes.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Convergence of stochastic processes

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.187985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3a083b08-7eec-4796-b903-cd55b4cb86aa · outbound

This paper cites Searching for Activation Functions.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Searching for Activation Functions

Reference 58

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Observation 8eef666b-4515-44f4-b3b1-e8069730d141 · outbound

This paper cites Introduction to differential geometry.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Introduction to differential geometry

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.172485Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7c087a87-a989-4ad0-b7cd-a5fe1d4fe818 · outbound

This paper cites Lagrange multipliers and optimality.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Lagrange multipliers and optimality

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.157264Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a944008e-0cfd-46e7-8bff-5558bee8739e · outbound

This paper cites Variational analysis, volume 317.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Variational analysis, volume 317

Reference 61

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Observation 45e9ca85-9460-49c4-bb49-2b94ffabb97c · outbound

This paper cites On the density of families of sets.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function On the density of families of sets

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-10T15:48:59.759725Z

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Observation 470fc8cc-310f-4e2e-867c-2de5c92f8ebf · outbound

This paper cites Understanding machine learning: From theory to algorithms.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Understanding machine learning: From theory to algorithms

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.120569Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0af78866-fe78-4d48-b2ff-96b4fe1baf03 · outbound

This paper cites Efficiently learning the graph for semi-supervised learning.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Efficiently learning the graph for semi-supervised learning

Reference 64

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Observation d909190b-ee7d-4314-954b-6c2b9c77f45a · outbound

This paper cites Practical B ayesian optimization of machine learning algorithms.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Practical B ayesian optimization of machine learning algorithms

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.085563Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 71cb00b4-fefd-4c9a-8cfe-420ab0cdf3ad · outbound

This paper cites Scalable B ayesian optimization using deep neural networks.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Scalable B ayesian optimization using deep neural networks

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.068335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1af26f61-9029-498f-b023-8151785cb1ec · outbound

This paper cites Graph attention networks.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Graph attention networks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.048770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 95a12886-dc91-4f0e-96be-0522e89ca11a · outbound

This paper cites High-dimensional statistics: A non-asymptotic viewpoint, volume 48.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function High-dimensional statistics: A non-asymptotic viewpoint, volume 48

Reference 68

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Source-reported events for the cited work

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Observation 294bc960-033a-413f-aa64-0c35144c6f6e · outbound

This paper cites Lower bounds for approximation by nonlinear manifolds.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Lower bounds for approximation by nonlinear manifolds

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.011557Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 21edacf1-6caf-4f1b-84c5-e680f0fea567 · outbound

This paper cites Bananas: B ayesian optimization with neural architectures for neural architecture search.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Bananas: B ayesian optimization with neural architectures for neural architecture search

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:48:59.992376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d0163511-dba1-4bb5-87e0-1ca922882f44 · outbound

This paper cites Simplifying graph convolutional networks.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Simplifying graph convolutional networks

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:48:59.979205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f5736f3c-826e-4214-85e6-aeaee3041901 · outbound

This paper cites Learning with local and global consistency.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Learning with local and global consistency

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:48:59.966078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6a8b2f28-7218-4d32-93dd-025a5cb015fe · outbound

This paper cites Semi-supervised learning with graphs.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Semi-supervised learning with graphs

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:48:59.952892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3e670abb-b847-4472-ab09-b33f98275c7c · outbound

This paper cites Semi-supervised learning using G aussian fields and harmonic functions.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Semi-supervised learning using G aussian fields and harmonic functions

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:48:59.940947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 94d702bf-b588-4727-8efe-4c9db3690726 · outbound

This paper cites Neural architecture search with reinforcement learning.

Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function Neural architecture search with reinforcement learning

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:48:59.926431Z

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