Fetching the paper…
Reading the bibliography…
The recent rise in popularity of Hyperparameter Optimization (HPO) for deep learning has highlighted the role that good hyperparameter (HP) space design can play in training strong models.
Generalized functional ANOVA diagnostics for high-dimensional functions of dependent variables
G. Hooker · 2007
Earlier work this paper cites.
Generalized functional ANOVA diagnostics for high-dimensional functions of dependent variables
G. Hooker · 2007
Earlier work this paper cites.
Algorithms for hyper-parameter optimization
J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl · 2011
Earlier work this paper cites.
Random search for hyper-parameter optimization
J. Bergstra and Y. Bengio · 2012
Earlier work this paper cites.
Practical bayesian optimization of machine learning algorithms
J. Snoek, H. Larochelle, and R. Adams · 2012
Earlier work this paper cites.
Direct divergence approximation between probability distributions and its applications in machine learning
M. Sugiyama, S. Liu, MC. Du Plessis, M. Yamanaka, M. Yamada, T. Suzuki, and T. Kanamori · 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.
An efficient approach for assessing hyperparameter importance
F. Hutter, H. Hoos, and K. Leyton-Brown · 2014
Earlier work this paper cites.
Efficient parameter importance analysis via ablation with surrogates
A. Biedenkapp, M. Lindauer, K. Eggensperger, F. Hutter, C. Fawcett, and H. Hoos · 2017
Earlier work this paper cites.
CAVE: Configuration assessment, visualization and evaluation
A. Biedenkapp, J. Marben, M. Lindauer, and F. Hutter · 2018
Cited alongside, same era.
Bayesian optimization in AlphaGo
Y. Chen, A. Huang, Z. Wang, I. Antonoglou, J. Schrittwieser, D. Silver, and N. de Freitas · 2018
Cited alongside, same era.
Deep reinforcement learning that matters
P. Henderson, R. Islam, P. Bachman, J. Pineau, D. Precup, and D. Meger · 2018
Cited alongside, same era.
On the state of the art of evaluation in neural language models
G. Melis, C. Dyer, and P. Blunsom · 2018
Cited alongside, same era.
CAVE: Configuration assessment, visualization and evaluation
A. Biedenkapp, J. Marben, M. Lindauer, and F. Hutter · 2018
Cited alongside, same era.
Optuna: A next-generation hyperparameter optimization framework
Auto-Pytorch: Multi-fidelity metalearning for efficient and robust AutoDL
L. Zimmer, M. Lindauer, and F. Hutter · 2021
Later among the works it cites.
Explaining hyperparameter optimization via partial dependence plots
J. Moosbauer, J. Herbinger, G. Casalicchio, M. Lindauer, and B. Bischl · 2021
Later among the works it cites.
TrivialAugment: Tuning-free yet state-of-the-art data augmentation
SG. Müller and F. Hutter · 2021
Later among the works it cites.
JAHS-Bench-201: A foundation for research on joint architecture and hyperparameter search
A. Bansal, D. Stoll, M. Janowski, A. Zela, and F. Hutter · 2022
Later among the works it cites.
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
Later among the works it cites.
DeepCAVE: An interactive analysis tool for automated machine learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama · 2019
Cited alongside, same era.
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
V. Perrone, H. Shen, MW. Seeger, C. Archambeau, and R. Jenatton · 2019
Cited alongside, same era.
NAS-Bench-201: Extending the scope of reproducible neural architecture search
X. Dong and Y. Yang · 2020
Cited alongside, same era.
On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling
K. Pearson
Cited in the paper.
On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling
K. Pearson
Cited in the paper.
R. Sass, E. Bergman, A. Biedenkapp, F. Hutter, and M. Lindauer · 2022
Later among the works it cites.
JAHS-Bench-201: A foundation for research on joint architecture and hyperparameter search
A. Bansal, D. Stoll, M. Janowski, A. Zela, and F. Hutter · 2022
Later among the works it cites.
S. Watanabe · 2023
Closest in time.