Fetching the paper…
Reading the bibliography…
Bayesian optimization (BO) is a sample efficient approach to automatically tune the hyperparameters of machine learning models.
High-dimensional Bayesian optimization using low-dimensional feature spaces
Moriconi, R., Deisenroth, M. P., and Kumar, K. S. S. (2019) · 1902
Earlier work this paper cites.
Tabular benchmarks for joint architecture and hyperparameter optimization
Klein, A. and Hutter, F. (2019) · 1905
Earlier work this paper cites.
A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise
Kushner, H. J. (1964) · 1964
Earlier work this paper cites.
The application of bayesian methods for seeking the extremum
Mockus, J., Tiesis, V., and Zilinskas, A. (1978) · 1978
Earlier work this paper cites.
Bayesian nonlinear modeling for the prediction competition
MacKay, D. J. et al. (1994) · 1994
Earlier work this paper cites.
A taxonomy of global optimization methods based on response surfaces
Jones, D. R. (2001) · 2001
Earlier work this paper cites.
SciPy: Open source scientific tools for Python
Jones, E., Oliphant, T., Peterson, P., et al. (2001–) · 2001
Earlier work this paper cites.
Sparse bayesian learning and the relevance vector machine
Tipping, M. E. (2001) · 2001
Earlier work this paper cites.
Gaussian processes in machine learning
Rasmussen, C. E. (2003) · 2003
Earlier work this paper cites.
Pattern recognition and machine learning
Bishop, C. M. (2006) · 2006
Earlier work this paper cites.
Mumbo: Multi-task max-value Bayesian optimization
Moss, H. B., Leslie, D. S., and Rayson, P. (2020) · 2006
Earlier work this paper cites.
Multi-task feature learning
Argyriou, A., Evgeniou, T., and Pontil, M. (2007) · 2007
Earlier work this paper cites.
A new view of automatic relevance determination
Wipf, D. W. and Nagarajan, S. (2008) · 2008
Earlier work this paper cites.
The knowledge-gradient policy for correlated normal beliefs
Frazier, P., Powell, W., and Dayanik, S. (2009) · 2009
Earlier work this paper cites.
Gaussian process optimization in the bandit setting: No regret and experimental design
Srinivas, N., Krause, A., Kakade, S. M., and Seeger, M. (2009) · 2009
Cited alongside, same era.
Algorithms for hyper-parameter optimization
Bergstra, J. S., Bardenet, R., Bengio, Y., and Kégl, B. (2011) · 2011
Cited alongside, same era.
Portfolio allocation for bayesian optimization
Hoffman, M. D., Brochu, E., and de Freitas, N. (2011) · 2011
Cited alongside, same era.
Sequential model-based optimization for general algorithm configuration
Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2011) · 2011
Cited alongside, same era.
Entropy search for information-efficient global optimization
Hennig, P. and Schuler, C. J. (2012) · 2012
Cited alongside, same era.
Practical bayesian optimization of machine learning algorithms
Initializing bayesian hyperparameter optimization via meta-learning
Feurer, M., Springenberg, J. T., and Hutter, F. (2015) · 2015
Later among the works it cites.
Spectral representations for convolutional neural networks
Rippel, O., Snoek, J., and Adams, R. P. (2015) · 2015
Later among the works it cites.
Taking the human out of the loop: A review of bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and De Freitas, N. (2015) · 2015
Later among the works it cites.
Scalable bayesian optimization using deep neural networks
Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M., Prabhat, M., and Adams, R. (2015) · 2015
Later among the works it cites.
Learning ordered word representations with γ \gamma -decay dropout
Liu, A., Xing, C., Feng, Y., and Wang, D. (2016) · 2016
Later among the works it cites.
Bayesian optimization with robust bayesian neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Snoek, J., Larochelle, H., and Adams, R. P. (2012) · 2012
Cited alongside, same era.
Multi-task bayesian optimization
Swersky, K., Snoek, J., and Adams, R. P. (2013) · 2012
Cited alongside, same era.
On the importance of initialization and momentum in deep learning
Sutskever, I., Martens, J., Dahl, G., and Hinton, G. (2013) · 2013
Cited alongside, same era.
Deep learning for real-time atari game play using offline monte-carlo tree search planning
Guo, X., Singh, S., Lee, H., Lewis, R. L., and Wang, X. (2014) · 2014
Cited alongside, same era.
Collaborative multi-output gaussian processes
Nguyen, T. V., Bonilla, E. V., et al. (2014) · 2014
Cited alongside, same era.
Learning ordered representations with nested dropout
Rippel, O., Gelbart, M., and Adams, R. (2014) · 2014
Cited alongside, same era.
Openml: networked science in machine learning
Vanschoren, J., Van Rijn, J. N., Bischl, B., and Torgo, L. (2014) · 2014
Cited alongside, same era.
Springenberg, J. T., Klein, A., Falkner, S., and Hutter, F. (2016) · 2016
Later among the works it cites.
An overview of multi-task learning in deep neural networks
Ruder, S. (2017) · 2017
Later among the works it cites.
A tutorial on bayesian optimization
Frazier, P. I. (2018) · 2018
Later among the works it cites.
Emukit: Emulation and uncertainty quantification for decision making
Paleyes, A., Pullin, M., Mahsereci, M., Lawrence, N., and González, J. (2018) · 2018
Later among the works it cites.
Scalable hyperparameter transfer learning
Perrone, V., Jenatton, R., Seeger, M. W., and Archambeau, C. (2018) · 2018
Later among the works it cites.
A framework for Bayesian optimization in embedded subspaces
A.Nayebi, Munteanu, A., and Poloczek, M. (2019) · 2019
Later among the works it cites.
Hyperparameter learning via distributional transfer
Law, H. C., Zhao, P., Chang, L. S., Huang, J., and Sejdinovic, D. (2019) · 2019
Later among the works it cites.
A quantile-based approach for hyperparameter transfer learning
Salinas, D., Shen, H., and Perrone, V. (2020) · 2020
Later among the works it cites.