Paper Citation Record · LEDGER

Automated Machine Learning in Practice: State of the Art and Recent Results

As of 4 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:1907.08392.

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

pith.paper-citation-record.v1
1907.08392 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T19:17:12.278286Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

verified exact3
verified fuzzy44
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation db84daa9-e9cb-4fe4-933e-ad3bbcaa893b · outbound

This paper cites Braschler, K.

Automated Machine Learning in Practice: State of the Art and Recent Results Braschler, K

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.596558Z

Source-reported events for the cited work

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Observation 17e0b18a-78ff-4f58-bac7-157d35c969b3 · outbound

This paper cites Learning neural models for end-to-end clustering.

Automated Machine Learning in Practice: State of the Art and Recent Results Learning neural models for end-to-end clustering

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.678422Z

Source-reported events for the cited work

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Observation 24df536c-1486-4349-8872-bc1a1c8d58e4 · outbound

This paper cites Automatic machine learn- ing: methods, systems, challenges.

Automated Machine Learning in Practice: State of the Art and Recent Results Automatic machine learn- ing: methods, systems, challenges

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.674507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 421288f5-3937-4414-aa19-50f5c1996c10 · outbound

This paper cites Auto- weka: Combined selection and hyperparameter optimization of clas- siﬁcation algorithms.

Automated Machine Learning in Practice: State of the Art and Recent Results Auto- weka: Combined selection and hyperparameter optimization of clas- siﬁcation algorithms

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.662280Z

Source-reported events for the cited work

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Observation 97c30b52-4d9d-4c08-ae94-380ec1dbd6ad · outbound

This paper cites Machine learning for predictive maintenance: A multiple classiﬁer approach.

Automated Machine Learning in Practice: State of the Art and Recent Results Machine learning for predictive maintenance: A multiple classiﬁer approach

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.585255Z

Source-reported events for the cited work

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Observation 9cf200ce-87d0-44ac-bd20-703fb8c2981e · outbound

This paper cites Improving rail network velocity: A machine learning approach to predictive maintenance.

Automated Machine Learning in Practice: State of the Art and Recent Results Improving rail network velocity: A machine learning approach to predictive maintenance

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.549470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 5a2e5df3-8460-41bd-afe2-7885322f50b6 · outbound

This paper cites Machine learning algorithms for damage detection under operational and environmental variability.

Automated Machine Learning in Practice: State of the Art and Recent Results Machine learning algorithms for damage detection under operational and environmental variability

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.576297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 84217c8a-2745-4db9-99b1-d45ac6b5e64f · outbound

This paper cites Frame- work for personalized prediction of treatment response in relapsing remitting multiple sclerosis.

Automated Machine Learning in Practice: State of the Art and Recent Results Frame- work for personalized prediction of treatment response in relapsing remitting multiple sclerosis

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.572736Z

Source-reported events for the cited work

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Observation 449b4dc2-cf64-4ca6-89cd-e2a7d9c2fb0c · outbound

This paper cites How neural networks can help loan ofﬁcers to make better informed application decisions.

Automated Machine Learning in Practice: State of the Art and Recent Results How neural networks can help loan ofﬁcers to make better informed application decisions

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.565053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation bd823f90-f965-412d-80e2-08dd65e787a2 · outbound

This paper cites Auto claim fraud detec- tion using bayesian learning neural networks.

Automated Machine Learning in Practice: State of the Art and Recent Results Auto claim fraud detec- tion using bayesian learning neural networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.616028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b0549507-8c4b-41dc-bb99-1aed5ec924bb · outbound

This paper cites Consolidated tree classiﬁer learning in a car insurance fraud detection domain with class imbalance.

Automated Machine Learning in Practice: State of the Art and Recent Results Consolidated tree classiﬁer learning in a car insurance fraud detection domain with class imbalance

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.553163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 94b538d9-54ae-41b1-9aa7-6a341da7f04c · outbound

This paper cites A survey of machine learning techniques for food sales prediction.

Automated Machine Learning in Practice: State of the Art and Recent Results A survey of machine learning techniques for food sales prediction

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.556993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 5077080e-ae91-4df4-8dae-bb5dc809f86a · outbound

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

Automated Machine Learning in Practice: State of the Art and Recent Results Hyperband: A novel bandit-based approach to hyperparameter opti- mization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.561451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 77150cde-2cbb-4d06-860b-bd07bfa4bb49 · outbound

This paper cites Automated generation and selection of interpretable features for enterprise security.

Automated Machine Learning in Practice: State of the Art and Recent Results Automated generation and selection of interpretable features for enterprise security

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.568751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation dced748a-d7ed-434b-a132-1ca1d7d47ef9 · outbound

This paper cites Learning to learn by gradient descent by gradient descent.

Automated Machine Learning in Practice: State of the Art and Recent Results Learning to learn by gradient descent by gradient descent

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.600175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 0fdc3484-a568-4c31-a8be-8309d9d2a447 · outbound

This paper cites Neural architecture search with reinforcement learning.

Automated Machine Learning in Practice: State of the Art and Recent Results Neural architecture search with reinforcement learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.538685Z

Source-reported events for the cited work

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Observation df4df77f-5765-4e71-a9db-b4238bb64ed0 · outbound

This paper cites Efﬁcient and robust automated machine learning.

Automated Machine Learning in Practice: State of the Art and Recent Results Efﬁcient and robust automated machine learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.593027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation f7c50aed-5c12-4701-a553-b356cb456efa · outbound

This paper cites Feature selection as a one-player game.

Automated Machine Learning in Practice: State of the Art and Recent Results Feature selection as a one-player game

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.542001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation dbcf8510-7d78-427c-a6ee-2863aa968198 · outbound

This paper cites Explorekit: Automatic feature generation and selection.

Automated Machine Learning in Practice: State of the Art and Recent Results Explorekit: Automatic feature generation and selection

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation f9de853d-7adc-48e3-b460-b54d0c0a7bd4 · outbound

This paper cites Learning feature engineering for classiﬁcation.

Automated Machine Learning in Practice: State of the Art and Recent Results Learning feature engineering for classiﬁcation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.612117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 00c78408-9b06-42a0-9779-3b83dd4c4dd2 · outbound

This paper cites Autolearnautomated feature generation and selection.

Automated Machine Learning in Practice: State of the Art and Recent Results Autolearnautomated feature generation and selection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.581516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 400fd4a4-3e1f-499d-b4d4-c2f2c1244943 · outbound

This paper cites Stability selection.

Automated Machine Learning in Practice: State of the Art and Recent Results Stability selection

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 7587f175-739e-41bd-9f94-aa86d5a3ec76 · outbound

This paper cites Meta-learning by landmarking various learning algorithms.

Automated Machine Learning in Practice: State of the Art and Recent Results Meta-learning by landmarking various learning algorithms

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.526807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 026459a9-1dcc-4fbf-837b-cb482a31e0c1 · outbound

This paper cites Learning curve prediction with bayesian neural networks.

Automated Machine Learning in Practice: State of the Art and Recent Results Learning curve prediction with bayesian neural networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.519654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation fccd808c-9f05-4525-8788-755f3e826f8d · outbound

This paper cites Neural networks for predicting algorithm runtime distributions.

Automated Machine Learning in Practice: State of the Art and Recent Results Neural networks for predicting algorithm runtime distributions

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.530723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 1737f22e-1ff8-4db6-92d0-7791b4842114 · outbound

This paper cites A comparison of ranking methods for classiﬁcation algorithm selection.

Automated Machine Learning in Practice: State of the Art and Recent Results A comparison of ranking methods for classiﬁcation algorithm selection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.545752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 1d3e328c-a7bb-4fb9-9869-506c543d36b6 · outbound

This paper cites Long short-term memory.

Automated Machine Learning in Practice: State of the Art and Recent Results Long short-term memory

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 396c721a-b58b-44ad-a9aa-91315e3b62ca · outbound

This paper cites Learning to learn without gradient descent by gradient descent.

Automated Machine Learning in Practice: State of the Art and Recent Results Learning to learn without gradient descent by gradient descent

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.589040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 9e16a202-96be-44c7-bd36-bedd2a57ddbb · outbound

This paper cites Support-vector networks.

Automated Machine Learning in Practice: State of the Art and Recent Results Support-vector networks

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b749af88-d32f-476e-8549-42a5f8abce31 · outbound

This paper cites Simple and efﬁcient archi- tecture search for convolutional neural networks.

Automated Machine Learning in Practice: State of the Art and Recent Results Simple and efﬁcient archi- tecture search for convolutional neural networks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.670536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation cae3ef00-748e-4da1-a516-31b7abf4fba5 · outbound

This paper cites Large-scale evolution of image classiﬁers.

Automated Machine Learning in Practice: State of the Art and Recent Results Large-scale evolution of image classiﬁers

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.654630Z

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No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 3114ec0d-86ea-4c0a-a899-5710d859ad32 · outbound

This paper cites Amc: Automl for model compression and acceleration on mobile devices.

Automated Machine Learning in Practice: State of the Art and Recent Results Amc: Automl for model compression and acceleration on mobile devices

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.650863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 6fe86955-1783-4569-a3f6-3ff7d68ff1a9 · outbound

This paper cites Analysis of the automl challenge series 2015-2018.

Automated Machine Learning in Practice: State of the Art and Recent Results Analysis of the automl challenge series 2015-2018

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.658639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 6cfd5fc7-7887-4b8b-a9e0-9ae937977ac7 · outbound

This paper cites A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning.

Automated Machine Learning in Practice: State of the Art and Recent Results A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

Reference 34

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No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 563f86a6-45ec-4e9e-8b65-90d85d7adbd6 · outbound

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

Automated Machine Learning in Practice: State of the Art and Recent Results Sequential model-based optimization for general algorithm conﬁguration

Reference 35

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No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 388f3d91-5163-45cc-9c5e-196d743cadeb · outbound

This paper cites Using meta-learning to initialize bayesian optimization of hyperparameters.

Automated Machine Learning in Practice: State of the Art and Recent Results Using meta-learning to initialize bayesian optimization of hyperparameters

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.647097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 32f76b70-0263-4450-8af6-809fbd9278dd · outbound

This paper cites Non-stochastic best arm identiﬁca- tion and hyperparameter optimization.

Automated Machine Learning in Practice: State of the Art and Recent Results Non-stochastic best arm identiﬁca- tion and hyperparameter optimization

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.635262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation c2487424-e78e-4428-ad31-2af8ea0519f9 · outbound

This paper cites Population Based Training of Neural Networks.

Automated Machine Learning in Practice: State of the Art and Recent Results Population Based Training of Neural Networks

Reference 38

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ab519dd3-afda-42e7-9b4b-1965f4a9490f · outbound

This paper cites Gradient-based hyper- parameter optimization through reversible learning.

Automated Machine Learning in Practice: State of the Art and Recent Results Gradient-based hyper- parameter optimization through reversible learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.604188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 3f580494-753d-423b-a388-e8f406d97054 · outbound

This paper cites Banzhaf, P.

Automated Machine Learning in Practice: State of the Art and Recent Results Banzhaf, P

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.685164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 8a1dd2db-76dd-430e-a1bd-df1f7baebfad · outbound

This paper cites The kernel trick for distances.

Automated Machine Learning in Practice: State of the Art and Recent Results The kernel trick for distances

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.631441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ca0dcfa2-ac56-440a-add0-5fc39c98c6a6 · outbound

This paper cites Atm: A distributed, collaborative, scalable system for automated machine learning.

Automated Machine Learning in Practice: State of the Art and Recent Results Atm: A distributed, collaborative, scalable system for automated machine learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.638940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 5f147242-a57d-45e8-8935-44d341d395e4 · outbound

This paper cites Practical automated machine learning for the automl challenge 2018.

Automated Machine Learning in Practice: State of the Art and Recent Results Practical automated machine learning for the automl challenge 2018

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.620124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 3b619896-c129-4aab-922e-e62e82603cd4 · outbound

This paper cites Deep learning in the wild.

Automated Machine Learning in Practice: State of the Art and Recent Results Deep learning in the wild

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.623815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b7316182-1fe9-452b-80bd-cb22d08e4d77 · outbound

This paper cites Automating biomedical data science through tree-based pipeline optimization.

Automated Machine Learning in Practice: State of the Art and Recent Results Automating biomedical data science through tree-based pipeline optimization

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.627687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation dab92f17-80dd-4125-9f9a-a5ae525e4cf8 · outbound

This paper cites Openml: Net- worked science in machine learning.

Automated Machine Learning in Practice: State of the Art and Recent Results Openml: Net- worked science in machine learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T19:19:51.643397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 92669ca1-ed22-47e8-9fad-a837c7bf6765 · outbound

This paper cites Learning to Optimize.

Automated Machine Learning in Practice: State of the Art and Recent Results Learning to Optimize

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-05-24T19:19:50.618895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Pith citing papers

No inbound Pith citation observations are available.