Paper Citation Record · LEDGER

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

As of 18 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 2 inbound Pith citation observations for arXiv:2501.13734.

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

pith.paper-citation-record.v1
2501.13734 v4

Coverage vector

measured 75 of 75 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:48:59.812014Z

measured 77 of 77 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:58:02.823148Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T06:27:07.361430Z

Reference resolution

75 of 75 outbound references displayed

verified exact1
verified fuzzy57
unresolved17
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4fd5e2c7-a47a-49e4-8428-b36fd9552bef · outbound

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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Source-reported events for the cited work

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Observation ac2252b5-4b6d-4f2d-a28a-dfb8355a6bed · outbound

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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Observation f32dacb3-a6b2-4070-b732-fea2139d4d09 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:01.061187Z

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Observation 735b7f21-1f35-41e4-876f-5912dac9a748 · outbound

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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Observation c64d8ddf-2fa7-43ea-aab3-e840b5d6b4a0 · outbound

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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Source-reported events for the cited work

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Observation 0248a75a-3289-4d7a-8298-bf13884d2b59 · outbound

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.984734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 54df198f-2562-4a46-bf02-b6738e711ad8 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.964703Z

Source-reported events for the cited work

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Observation 666b61aa-fc3c-447e-970e-4ef04946884d · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.937261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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Source-reported events for the cited work

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Observation 563d8b00-54b9-41a2-be09-5f7ba0c61e6b · outbound

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7a8f5ffe-70fd-41ed-a94f-a3faf96c500f · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.889042Z

Source-reported events for the cited work

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Observation 79cd4eb8-c659-45d0-ae2b-ee40783ac83e · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-10T15:48:59.558316Z

Source-reported events for the cited work

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Observation 388c67e8-6b82-419f-83b0-eee04b9292a4 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.862433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 002accf5-e939-4c38-a32d-165c397ee65d · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.847620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f470decf-8e19-4673-a632-7999d6e23c94 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.834024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9d55c0cb-f191-470f-9076-4c50212918e8 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.816649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4eb18dbe-906b-4600-a436-194cb61bd1d2 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.794818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7c226fec-a3d2-4166-b6c0-d318e5fe6099 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.775473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6b0950fe-1410-4b58-bf41-6606be57c115 · outbound

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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Source-reported events for the cited work

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Observation a7b7c9f7-0204-4974-b97c-fd9118010e0f · outbound

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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Source-reported events for the cited work

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Observation ddff3e9c-e6b7-4243-8b30-5af265e82dd6 · outbound

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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Source-reported events for the cited work

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Observation 5699e6cb-8925-4e9b-8bc3-4e1d45acd16e · outbound

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 41c48274-16bc-40fe-b8ba-c728e2aa7213 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.697359Z

Source-reported events for the cited work

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Observation 91560ea1-9e51-4dcd-8347-2c76c009381c · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.682053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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Source-reported events for the cited work

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Observation 75287953-c9e7-4ad4-9c43-df4435672142 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.653802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bd2881a0-1975-47ad-a1ec-f8b5264569d5 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.634167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6985efe1-a6da-48e0-9c5e-a2af5c56cd61 · outbound

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6aacbe5d-ac83-49c3-b8e0-a2bbd8e0fe1c · outbound

This paper cites Advanced calculus.

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3dc9a4f3-dd3d-4c0b-ba09-9e195a0bb595 · outbound

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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Source-reported events for the cited work

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Observation 95471c8c-b842-4570-8a7b-235a186577dd · outbound

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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Observation e15403c5-39b2-42e7-86b4-325a2346bdab · outbound

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5bf709e9-b166-4572-ba84-4798ea8192e4 · outbound

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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Observation f07450c5-b2cf-49eb-8ee6-9378143a0284 · outbound

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dfd8170d-0aeb-43e7-a4ae-8a84c4937b57 · outbound

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 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d4790a7c-1fa2-4e96-b2e9-b281aed0cdb3 · outbound

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e5daabb8-8c67-4ef6-b27c-07e90df80b05 · outbound

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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Source-reported events for the cited work

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Observation 01130de3-d321-44b0-91bb-25dc873f38c1 · outbound

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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Source-reported events for the cited work

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Observation 64801833-5a10-434f-9bb1-20a45bca1fa1 · outbound

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 15b4d3c1-489c-4c69-ac16-0c8c4e0ae831 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.406008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.387734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d4e30e40-2676-492d-bced-17264f2f489c · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.370004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 83d4c457-08ac-46af-8697-2134aa594615 · outbound

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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Source-reported events for the cited work

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Observation 7aace2aa-18b4-4ffa-a747-dd9a64f8005f · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.344431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d43bc131-28dc-4945-a177-098b3d9d44b3 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-10T15:48:59.699580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf0b1ce0-3b5e-40c5-b508-74955cc53b27 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.318800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.303586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 366f1c86-0759-485e-84f5-43248c5bce0a · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.288612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.270932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:49:00.254313Z

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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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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

Resolution: unresolved; no resolver link, observed 2026-08-10T15:48:59.731476Z

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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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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

Resolution: unresolved; no resolver link, observed 2026-08-10T15:48:59.743447Z

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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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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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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

Resolution: unresolved; no resolver link, observed 2026-08-10T15:48:59.755964Z

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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 2026-08-18T06:34:40.430872+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

Resolution: unresolved; no resolver link, observed 2026-08-10T15:48:59.767174Z

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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 2026-08-18T06:34:40.430872+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

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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 2026-08-18T06:34:40.430872+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

Resolution: unresolved; no resolver link, observed 2026-08-10T15:48:59.784355Z

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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 2026-08-18T06:34:40.430872+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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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

Source-reported events for the cited work

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Pith citing papers

Observation 157374ac-d899-45ec-ad6c-765fe1dafd87 · inbound

Generalization Guarantees for Learning Branch-and-Cut Policies in Integer Programming cites this paper.

Generalization Guarantees for Learning Branch-and-Cut Policies in Integer Programming Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:58:02.823148Z

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Observation a989bf67-1dba-44e2-b243-ae1c0d4b8eab · inbound

Distribution-dependent Generalization Bounds for Tuning Linear Regression Across Tasks cites this paper.

Distribution-dependent Generalization Bounds for Tuning Linear Regression Across Tasks Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-19T06:27:07.363593Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.