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Nuclear fusion is regarded as the energy of the future since it presents the possibility of unlimited clean energy.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
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Bayesian optimization with expensive integrands
Saul Toscano-Palmerin and Peter I Frazier · 2001
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Using confidence bounds for exploitation-exploration trade-offs
Peter Auer · 2002
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series)
Carl Edward Rasmussen and Christopher K. I. Williams · 2005
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X-armed bandits
Sébastien Bubeck, Rémi Munos, Gilles Stoltz, and Csaba Szepesvári · 2011
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An indispensable truth: how fusion power can save the planet
Francis Chen · 2011
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Contextual gaussian process bandit optimization
Andreas Krause and Cheng S Ong · 2011
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Thompson sampling for contextual bandits with linear payoffs
Shipra Agrawal and Navin Goyal · 2013
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Automatic disruption classification based on manifold learning for real-time applications on jet
Barbara Cannas, Alessandra Fanni, A Murari, Alessandro Pau, Giuliana Sias, and JET EFDA Contributors · 2013
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Multi-task bayesian optimization
Kevin Swersky, Jasper Snoek, and Ryan P Adams · 2013
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A Piece of the sun: the quest for fusion energy
Daniel Clery · 2014
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Bayesian adaptive reconstruction of profile optima and optimizers
David Ginsbourger, Jean Baccou, Clément Chevalier, Frédéric Perales, Nicolas Garland, and Yann Monerie · 2014
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An information-theoretic analysis of thompson sampling
Daniel Russo and Benjamin Van Roy · 2016
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Pareto frontier learning with expensive correlated objectives
Amar Shah and Zoubin Ghahramani · 2016
Orchestrating transp simulations for interpretative and predictive tokamak modeling with omfit
BA Grierson, X Yuan, M Gorelenkova, S Kaye, NC Logan, O Meneghini, SR Haskey, J Buchanan, M Fitzgerald, SP Smith, et al · 2018
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Parallelised bayesian optimisation via thompson sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, and Barnabás Póczos · 2018
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Continuous multi-task bayesian optimisation with correlation
Michael Pearce and Juergen Branke · 2018
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Offline contextual bayesian optimization
Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark D Boyer, Egemen Kolemen, and Jeff Schneider · 2019
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Myopic posterior sampling for adaptive goal oriented design of experiments
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, and Barnabas Poczos · 2019
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Big data machine learning for disruption predictions
William Tang, Matthew Parsons, Eliot Feibush, A Murari, J Vega, A Pereira, and J Choi · 2016
Cited alongside, same era.
Achievement of sustained net plasma heating in a fusion experiment with the optometrist algorithm
EA Baltz, E Trask, M Binderbauer, M Dikovsky, H Gota, R Mendoza, JC Platt, and PF Riley · 2017
Cited alongside, same era.
Predicting disruptive instabilities in controlled fusion plasmas through deep learning
Julian Kates-Harbeck, Alexey Svyatkovskiy, and William Tang · 2019
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Machine learning for disruption warning on alcator c-mod, diii-d, and east
Kevin Joseph Montes, Cristina Rea, Robert Granetz, Roy Alexander Tinguely, Nicholas W Eidietis, O Meneghini, Dalong Chen, Biao Shen, Bingjia Xiao, Keith Erickson, et al · 2019
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