Automated Machine Learning

2019 · The Springer series on challenges in machine learning · 1,451 citations · 2 from inside this corpus

Frank Hutter, Lars Kotthoff, Joaquin Vanschoren

This open access book gives the first comprehensive overview of general methods in Automatic Machine Learning, AutoML, collects descriptions of existing AutoML systems based on these methods, and discusses the first international challenge of AutoML systems.

36 of 38 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

this paper works it cites works citing it node size = global citations · hover for the full title

What this paper cites, inside the corpus

Paper	Year	Cited
Adam: A Method for Stochastic Optimization	2014	84,698
Scikit-learn: Machine Learning in Python	2012	64,025
XGBoost	2016	52,304
Dropout: a simple way to prevent neural networks from overfitting	2014	34,236
Greedy function approximation: A gradient boosting machine.	2001	30,137
A Survey on Transfer Learning	2009	23,670
Xception: Deep Learning with Depthwise Separable Convolutions	2017	19,457
Mastering the game of Go with deep neural networks and tree search	2016	16,014
Sequence to Sequence Learning with Neural Networks	2014	13,351
The Elements of Statistical Learning: Data Mining, Inference, and Prediction	2010	12,807
Statistical Comparisons of Classifiers over Multiple Data Sets	2006	11,215
Automatic differentiation in PyTorch	2017	11,099
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection	1995	10,743
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization	2010	8,621
Pattern Recognition and Machine Learning	2007	8,431
Ensemble Methods in Machine Learning	2000	8,058
Stacked generalization	1992	7,777
Proceedings of the 25th international conference on Machine learning - ICML '08	2008	6,718
MICE: Multivariate Imputation by Chained Equations in R	2010	6,250
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks	2017	5,794
Practical Bayesian Optimization of Machine Learning Algorithms	2012	5,674
Proceedings of the 25th international conference on Machine learning	2008	5,551
ADADELTA: An Adaptive Learning Rate Method	2012	5,532
Prototypical Networks for Few-shot Learning	2017	5,204
mixup: Beyond Empirical Risk Minimization	2017	4,806
Neural Architecture Search with Reinforcement Learning	2016	3,881
Selection of relevant features and examples in machine learning	1997	3,309
Evolving Neural Networks through Augmenting Topologies	2002	3,298
Towards A Rigorous Science of Interpretable Machine Learning	2017	3,190
Algorithms for Hyper-Parameter Optimization	2011	3,188
One-shot learning of object categories	2006	3,095
Evolving artificial neural networks	1999	3,011
Optimization as a Model for Few-Shot Learning	2017	2,436
A Comparative Analysis of Selection Schemes Used in Genetic Algorithms	1991	2,414

What cites it, inside the corpus

Paper	Year	Cited
Optuna	2019	8,126
[No title in the source record — Edinburgh Research Explorer (University of Edinburgh)]		1,853

Links

DOI · Open access copy · OpenAlex record

Topics

Machine Learning and Data Classification

Computer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

supports3 author record(s) attached.
supports463 reference(s) recorded.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:58+00:00.

sha256 88a60cdbb94c6a13…