Fetching the paper…
Reading the bibliography…
Automated Machine Learning (AutoML) is used more than ever before to support users in determining efficient hyperparameters, neural architectures, or even full machine learning pipelines.
Learning internal representations by error propagation
D. Rumelhart, G. Hinton, and R. Williams · 1985
Earlier work this paper cites.
Nonlinear multiobjective optimization , volume 12 of International series in operations research and management science
K. Miettinen · 1998
Earlier work this paper cites.
Hics: High contrast subspaces for density-based outlier ranking
F. Keller, E. Muller, and K. Bohm · 2012
Earlier work this paper cites.
Fast gradient-based inference with continuous latent variable models in auxiliary form
D. Kingma · 2013
Earlier work this paper cites.
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms
C. Thornton, F. Hutter, H. Hoos, and K. Leyton-Brown · 2013
Earlier work this paper cites.
An efficient approach for assessing hyperparameter importance
F. Hutter, H. Hoos, and K. Leyton-Brown · 2014
Earlier work this paper cites.
Collaborative data science, 2015
Plotly Technologies Inc · 2015
Earlier work this paper cites.
Non-stochastic best arm identification and hyperparameter optimization
K. Jamieson and A. Talwalkar · 2016
Earlier work this paper cites.
Fast Bayesian optimization of machine learning hyperparameters on large datasets
A. Klein, S. Falkner, S. Bartels, P. Hennig, and F. Hutter · 2017
Earlier work this paper cites.
CAVE: Configuration assessment, visualization and evaluation
A. Biedenkapp, J. Marben, M. Lindauer, and F. Hutter · 2018
Earlier work this paper cites.
BOHB: Robust and efficient hyperparameter optimization at scale
S. Falkner, A. Klein, and F. Hutter · 2018
Cited alongside, same era.
Hyperband: A novel bandit-based approach to hyperparameter optimization
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar · 2018
Cited alongside, same era.
Deep autoencoding gaussian mixture model for unsupervised anomaly detection
B. Zong, Q. Song, M. Renqiang Min, W. Cheng, C. Lumezanu, D. Cho, and H. Chen · 2018
Cited alongside, same era.
Neural architecture search: A survey
T. Elsken, J. Metzen, and F. Hutter · 2019
Cited alongside, same era.
Automated Machine Learning: Methods, Systems, Challenges
F. Hutter, L. Kotthoff, and J. Vanschoren, editors · 2019
Cited alongside, same era.
Benchmarking discrete optimization heuristics with IOHprofiler
C. Doerr, F. Ye, N. Horesh, H. Wang, O. Shir, and T. Bäck · 2020
A comprehensive survey on hardware-aware neural architecture search
H. Benmeziane, K. El Maghraoui, H. Ouarnoughi, S. Niar, M. Wistuba, and N. Wang · 2021
Later among the works it cites.
Hyperparameter optimization: Foundations, algorithms, best practices and open challenges
B. Bischl, M. Binder, M. Lang, T. Pielok, J. Richter, S. Coors, J. Thomas, T. Ullmann, M. Becker, A. Boulesteix, D. Deng, and M. Lindauer · 2021
Later among the works it cites.
DASVDD: deep autoencoding support vector data descriptor for anomaly detection
H. Hojjati and N. Armanfard · 2021
Later among the works it cites.
Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
L. Zimmer, M. Lindauer, and F. Hutter · 2021
Later among the works it cites.
Auto-sklearn 2.0: The next generation
M. Feurer, K. Eggensperger, S. Falkner, M. Lindauer, and F. Hutter · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Trust in AutoML: exploring information needs for establishing trust in automated machine learning systems
J. Drozdal, J. D. Weisz, D. Wang, G. Dass, B. Yao, C. Zhao, M. J. Muller, L. Ju, and H. Su · 2020
Cited alongside, same era.
Best practices for scientific research on neural architecture search
M. Lindauer and F. Hutter · 2020
Cited alongside, same era.
Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence
S. Raschka, J. Patterson, and C. Nolet · 2020
Cited alongside, same era.
DEHB: Evolutionary hyberband for scalable, robust and efficient hyperparameter optimization
N. Awad, N. Mallik, and F. Hutter · 2021
Cited alongside, same era.
Closest in time.
Why do machine learning practitioners still use manual tuning? A qualitative study
N. Hasebrook, F. Morsbach, N. Kannengießer, J. Franke, F. Hutter, and A. Sunyaev · 2022
Closest in time.
Smac3: A versatile bayesian optimization package for hyperparameter optimization
M. Lindauer, K. Eggensperger, M. Feurer, A. Biedenkapp, D. Deng, C. Benjamins, T. Ruhkopf, R. Sass, and F. Hutter · 2022
Closest in time.
Retrospectives from 20 years of JMLR, 2022
F. Pedregosa, T. Maharaj, A. Kucukelbir, R. Das, V. Borghesani, F. Bach, D. Blei, and B. Schölkopf · 2022
Closest in time.
XAutoML: A Visual Analytics Tool for Establishing Trust in Automated Machine Learning
M. Zöller, W. Titov, T. Schlegel, and M. F. Huber · 2022
Closest in time.