Fetching the paper…
Reading the bibliography…
In this paper we develop a dynamic form of Bayesian optimization for machine learning models with the goal of rapidly finding good hyperparameter settings.
The application of Bayesian methods for seeking the extremum
Jonas Mockus, Vytautas Tiešis, and Antanas Žilinskas · 1978
Earlier work this paper cites.
Application of Bayesian approach to numerical methods of global and stochastic optimization
Jonas Mockus · 1994
Earlier work this paper cites.
An algorithmic framework for performing collaborative filtering
Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers, and John Riedl · 1999
Earlier work this paper cites.
A taxonomy of global optimization methods based on response surfaces
Donald R. Jones · 2001
Earlier work this paper cites.
Practical Bayesian Optimization
Dan Lizotte · 2008
Earlier work this paper cites.
Probabilistic matrix factorization
Ruslan Salakhutdinov and Andriy Mnih · 2008
Earlier work this paper cites.
Gaussian processes for global optimization
Michael A. Osborne, Roman Garnett, and Stephen J. Roberts · 2009
Earlier work this paper cites.
Handling sparsity via the horseshoe
Carlos M. Carvalho, Nicholas G. Polson, and James G. Scott · 2009
Earlier work this paper cites.
Slice sampling covariance hyperparameters of latent Gaussian models
Iain Murray and Ryan P. Adams · 2010
Earlier work this paper cites.
Gaussian process optimization in the bandit setting: no regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham Kakade, and Matthias Seeger · 2010
Cited alongside, same era.
Bayesian optimization for sensor set selection
Roman Garnett, Micheal A. Osborne, and Stephen J. Roberts · 2010
Cited alongside, same era.
A tutorial on Bayesian optimization of expensive cost functions
Eric Brochu, Vlad M. Cora, and Nando de Freitas · 2010
Cited alongside, same era.
Online learning for latent Dirichlet allocation
Matthew Hoffman, David M. Blei, and Francis Bach · 2010
Cited alongside, same era.
Algorithms for hyper-parameter optimization
James S. Bergstra, Rémi Bardenet, Yoshua Bengio, and Bálázs Kégl · 2011
Cited alongside, same era.
Convergence rates of efficient global optimization algorithms
Random search for hyper-parameter optimization
James S. Bergstra and Yoshua Bengio · 2012
Later among the works it cites.
Practical Bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P. Adams · 2012
Later among the works it cites.
Exponential regret bounds for Gaussian process bandits with deterministic observations
Nando de Freitas, Alex J. Smola, and Masrour Zoghi · 2012
Later among the works it cites.
Entropy search for information-efficient global optimization
Philipp Hennig and Christian J. Schuler · 2012
Later among the works it cites.
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2012
Later among the works it cites.
Multi-task Bayesian optimization
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Adam D. Bull · 2011
Cited alongside, same era.
Portfolio allocation for Bayesian optimization
Matthew Hoffman, Eric Brochu, and Nando de Freitas · 2011
Cited alongside, same era.
Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown · 2011
Cited alongside, same era.
A reliable effective terascale linear learning system, 2011
Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, and John Langford · 2011
Cited alongside, same era.
Kevin Swersky, Jasper Snoek, and Ryan Prescott Adams · 2013
Later among the works it cites.
Bayesian optimization with unknown constraints
Michael A. Gelbart, Jasper Snoek, and Ryan P. Adams · 2014
Closest in time.
Input warping for bayesian optimization of non-stationary functions
Jasper Snoek, Kevin Swersky, Richard S. Zemel, and Ryan P. Adams · 2014
Closest in time.