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Reinforcement learning algorithms can show strong variation in performance between training runs with different random seeds.
The application of bayesian methods for seeking the extremum
Jonas Mockus, Vytautas Tiesis, and Antanas Zilinskas · 1978
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Neuronlike adaptive elements that can solve difficult learning control problems
Andrew G Barto, Richard S Sutton, and Charles W Anderson · 1983
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Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es)
Nikolaus Hansen, Sibylle D Müller, and Petros Koumoutsakos · 2003
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Practical multi-fidelity bayesian optimization for hyperparameter tuning
Jian Wu, Saul Toscano-Palmerin, Peter I Frazier, and Andrew Gordon Wilson · 2007
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Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M Kakade, and Matthias Seeger · 2009
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Algorithms for hyper-parameter optimization
James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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The correlated knowledge gradient for simulation optimization of continuous parameters using gaussian process regression
Warren Scott, Peter Frazier, and Warren Powell · 2011
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Entropy search for information-efficient global optimization
Philipp Hennig and Christian J Schuler · 2012
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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A benchmark of kriging-based infill criteria for noisy optimization
Victor Picheny, Tobias Wagner, and David Ginsbourger · 2013
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Predictive entropy search for efficient global optimization of black-box functions
José Miguel Hernández-Lobato, Matthew W Hoffman, and Zoubin Ghahramani · 2014
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GPyOpt: A bayesian optimization framework in python
The GPyOpt authors · 2016
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 2016
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Non-stochastic best arm identification and hyperparameter optimization
Kevin Jamieson and Ameet Talwalkar · 2016
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2016
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Max-value entropy search for efficient bayesian optimization
Zi Wang and Stefanie Jegelka · 2017
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How many random seeds? statistical power analysis in deep reinforcement learning experiments
Cédric Colas, Olivier Sigaud, and Pierre-Yves Oudeyer · 2018
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Bohb: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
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A tutorial on bayesian optimization
Peter I Frazier · 2018
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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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Ilya Loshchilov and Frank Hutter · 2016
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The parallel knowledge gradient method for batch bayesian optimization
Jian Wu and Peter Frazier · 2016
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Reproducibility of benchmarked deep reinforcement learning tasks for continuous control
Riashat Islam, Peter Henderson, Maziar Gomrokchi, and Doina Precup · 2017
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Population based training of neural networks
Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, et al · 2017
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Multi-information source optimization
Matthias Poloczek, Jialei Wang, and Peter Frazier · 2017
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Large-scale evolution of image classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V Le, and Alexey Kurakin · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Stable baselines
Ashley Hill, Antonin Raffin, Maximilian Ernestus, Adam Gleave, Anssi Kanervisto, Rene Traore, Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 2018
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Massively parallel hyperparameter tuning
Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, and Ameet Talwalkar · 2018
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The impact of nondeterminism on reproducibility in deep reinforcement learning
Prabhat Nagarajan, Garrett Warnell, and Peter Stone · 2018
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Botorch: Programmable bayesian optimization in pytorch
Maximilian Balandat, Brian Karrer, Daniel R Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, and Eytan Bakshy · 2019
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CMA-ES/pycma on Github
Nikolaus Hansen, Youhei Akimoto, and Petr Baudis · 2019
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Constrained bayesian optimization with noisy experiments
Benjamin Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy, et al · 2019
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Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 2019
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