Fetching the paper…
Reading the bibliography…
Bayesian optimization (BO) has become an established framework and popular tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
W. Thompson · 1933
Earlier work this paper cites.
Spatial variation
B. Matérn · 1960
Earlier work this paper cites.
A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise
H. J. Kushner · 1964
Earlier work this paper cites.
The application of Bayesian methods for seeking the extremum
J. Mockus, V. Tiesis, and A. Zilinskas · 1978
Earlier work this paper cites.
Efficient global optimization of expensive black-box functions
D. Jones, M. Schonlau, and W. Welch · 1998
Earlier work this paper cites.
A taxonomy of global optimization methods based on response surfaces
D. R. Jones · 2001
Earlier work this paper cites.
Gaussian Processes for Machine Learning
C. Rasmussen and C. Williams · 2006
Earlier work this paper cites.
A knowledge-gradient policy for sequential information collection
P. Frazier, W. Powell, and S. Dayanik · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
E. Brochu, V. Cora, and N. de Freitas · 2010
Earlier work this paper cites.
An integrated micro-and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy
A. Cardona, S. Saalfeld, S. Preibisch, B. Schmid, A. Cheng, J. Pulokas, P. Tomancak, and V. Hartenstein · 2010
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.
Convergence rates of efficient global optimization algorithms
A. D. Bull · 2011
Earlier work this paper cites.
Sequential model-based optimization for general algorithm configuration
F. Hutter, H. Hoos, and K. Leyton-Brown · 2011
Earlier work this paper cites.
Random search for hyper-parameter optimization
J. Bergstra and Y. Bengio · 2012
Earlier work this paper cites.
Entropy search for information-efficient global optimization
P. Hennig and C. J. Schuler · 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.
Information-theoretic regret bounds for gaussian process optimization in the bandit setting
N. Srinivas, A. Krause, S. M. Kakade, and M. W. Seeger · 2012
Earlier work this paper cites.
Multi-task Bayesian optimization
K. Swersky, J. Snoek, and R. Adams · 2013
Earlier work this paper cites.
Bayesian gait optimization for bipedal locomotion
R. Calandra, N. Gopalan, A. Seyfarth, J. Peters, and M. Deisenroth · 2014
Earlier work this paper cites.
Predictive entropy search for efficient global optimization of black-box functions
J. M. Hernández-Lobato, M. W. Hoffman, and Z. Ghahramani · 2014
Earlier work this paper cites.
Input warping for bayesian optimization of non-stationary functions
J. Snoek, K. Swersky, R. Zemel, and R. Adams · 2014
Cited alongside, same era.
OpenML: Networked science in machine learning
J. Vanschoren, J. van Rijn, B. Bischl, and L. Torgo · 2014
Cited alongside, same era.
Crowdsourcing the creation of image segmentation algorithms for connectomics
I. Arganda-Carreras, S.C. Turaga, D.R. Berger, D. Cireşan, A. Giusti, L.M. Gambardella, J. Schmidhuber, D. Laptev, S. Dwivedi, J.M. Buhmann, T. Liu, M. Seyedhosseini, T. Tasdizen, L. Kamentsky, R. Burget, V. Uher, X. Tan, C. Sun, T.D. Pham, E. Bas, M.G. Uzunbas, A. Cardona, J. Schindelin, and H.S. Seung · 2015
Cited alongside, same era.
Initializing bayesian hyperparameter optimization via meta-learning
M. Feurer, Jost Tobias Springenberg, and F. Hutter · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
Meta-surrogate benchmarking for hyperparameter optimization
A. Klein, Z. Dai, F. Hutter, N. Lawrence, and J. Gonzalez · 2019
Later among the works it cites.
Quantifying the carbon emissions of machine learning
A. Lacoste, A. Luccioni, V. Schmidt, and T. Dandres · 2019
Later among the works it cites.
Practical design space exploration
L. Nardi, D. Koeplinger, and K. Olukotun · 2019
Later among the works it cites.
Emulation of physical processes with emukit
Andrei Paleyes, Mark Pullin, Maren Mahsereci, Neil Lawrence, and Javier González · 2019
Later among the works it cites.
Learning search spaces for bayesian optimization: Another view of hyperparameter transfer learning
V. Perrone, H. Shen, M. Seeger, C. Archambeau, and R. Jenatton · 2019
Later among the works it cites.
Atmseer: Increasing transparency and controllability in automated machine learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
O. Ronneberger, P. Fischer, and T. Brox · 2015
Cited alongside, same era.
Hyperparameter search space pruning - A new component for sequential model-based hyperparameter optimization
M. Wistuba, N. Schilling, and L. Schmidt-Thieme · 2015
Cited alongside, same era.
The iRace package: Iterated racing for automatic algorithm configuration
M. López-Ibáñez, J. Dubois-Lacoste, L. P. Cáceres, T. Stützle, and M. Birattari · 2016
Cited alongside, same era.
Bayesian optimization with robust Bayesian neural networks
J. Springenberg, A. Klein, S. Falkner, and F. Hutter · 2016
Cited alongside, same era.
GPyOpt: A bayesian optimization framework in python
The GPyOpt authors · 2016
Cited alongside, same era.
Improving Bayesian Optimization for Machine Learning using Expert Priors
K. J. Swersky · 2017
Cited alongside, same era.
Max-value entropy search for efficient bayesian optimization
Z. Wang and S. Jegelka · 2017
Cited alongside, same era.
Q. Wang, Y. Ming, Z. Jin, Q. Shen, D. Liu, M. J. Smith, K. Veeramachaneni, and H. Qu · 2019
Later among the works it cites.
Black magic in deep learning: How human skill impacts network training
K. Anand, Z. Wang, M. Loog, and J. van Gemert · 2020
Later among the works it cites.
Combining sequential model-based algorithm configuration with default-guided probabilistic sampling
M. Anastacio and H. Hoos · 2020
Later among the works it cites.
Botorch: A framework for efficient monte-carlo bayesian optimization
M. Balandat, B. Karrer, D. R. Jiang, S. Daulton, B. Letham, A. G. Wilson, and E. Bakshy · 2020
Later among the works it cites.
Survey of machine-learning experimental methods at NeurIPS2019 and ICLR2020
X. Bouthillier and G. Varoquaux · 2020
Later among the works it cites.
Tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly
K. Kandasamy, K. R. Vysyaraju, W. Neiswanger, B. Paria, C. R. Collins, J. Schneider, B. Poczos, and E. P. Xing · 2020
Later among the works it cites.
Cost-aware bayesian optimization, 2020
Eric Hans Lee, Valerio Perrone, Cedric Archambeau, and Matthias Seeger · 2020
Later among the works it cites.
Incorporating expert prior knowledge into experimental design via posterior sampling
C. Li, S. Gupta, S. Rana, V. Nguyen, A. Robles-Kelly, and S. Venkatesh · 2020
Later among the works it cites.
Incorporating expert prior in bayesian optimisation via space warping
A. Ramachandran, S. Gupta, S. Rana, C. Li, and S. Venkatesh · 2020
Later among the works it cites.
Hyperparameter transfer across developer adjustments
D. Stoll, J. KH Franke, D. Wagner, S. Selg, and F. Hutter · 2020
Later among the works it cites.
HPOBench: a collection of reproducible multi-fidelity benchmark problems for HPO, 2021
K. Eggensperger, P. Müller, N. Mallik, M. Feurer, R. Sass, A. Klein, N. Awad, M. Lindauer, and F. Hutter · 2021
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, R. Sass, and F. Hutter · 2021
Later among the works it cites.
Interpretable neural architecture search via bayesian optimisation with weisfeiler-lehman kernels
B. Ru, X. Wan, X. Dong, and M. Osborne · 2021
Later among the works it cites.
Bayesian optimization with a prior for the optimum
A. Souza, L. Nardi, L. Oliveira, K. Olukotun, M. Lindauer, and F. Hutter · 2021
Later among the works it cites.
Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020
R. Turner, D. Eriksson, M. McCourt, J. Kiili, E. Laaksonen, Z. Xu, and I. Guyon · 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.