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Bayesian Optimisation for Mixed-Variable Inputs using Value Proposals.

Y. Zuo, A. Dezfouli, I. Chades, D. Alexander, and B. Muir. CoRR, (2022)

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Two Approximate Dynamic Programming Algorithms for Managing Complete SIS Networks.M. Péron, P. Bartlett, K. Becker, K. Helmstedt, and I. Chadès. COMPASS, page 8:1-8:10. ACM, (2018)Bayesian Optimisation for Mixed-Variable Inputs using Value Proposals.Y. Zuo, A. Dezfouli, I. Chades, D. Alexander, and B. Muir. CoRR, (2022)Solving K-MDPs.J. Ferrer-Mestres, T. Dietterich, O. Buffet, and I. Chadès. ICAPS, page 110-118. AAAI Press, (2020)α-min: A Compact Approximate Solver For Finite-Horizon POMDPs.Y. Dujardin, T. Dietterich, and I. Chades. IJCAI, page 2582-2588. AAAI Press, (2015)From climate change to pandemics: decision science can help scientists have impact.C. Baker, P. Campbell, I. Chades, A. Dean, S. Hester, M. Holden, J. McCaw, J. McVernon, R. Moss, F. Shearer and 1 other author(s). CoRR, (2020)Mixed-Variable Black-Box Optimisation Using Value Proposal Trees.Y. Zuo, V. Nguyen, A. Dezfouli, D. Alexander, B. Muir, and I. Chades. AAAI, page 11506-11514. AAAI Press, (2023)Cross-Entropy Estimators for Sequential Experiment Design with Reinforcement Learning.T. Blau, E. Bonilla, I. Chades, and A. Dezfouli. CoRR, (2023)Fast-Tracking Stationary MOMDPs for Adaptive Management Problems.M. Péron, K. Becker, P. Bartlett, and I. Chades. AAAI, page 4531-4537. AAAI Press, (2017)Optimizing Sequential Experimental Design with Deep Reinforcement Learning.T. Blau, E. Bonilla, I. Chades, and A. Dezfouli. ICML, volume 162 of Proceedings of Machine Learning Research, page 2107-2128. PMLR, (2022)Algorithmes de co-évolution pour la résolution approchée de PDM multi-agent.I. Chades. Rev. d'Intelligence Artif., 20 (2-3): 345-382 (2006)

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Bayesian Optimisation for Mixed-Variable Inputs using Value Proposals.

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Disambiguation of "Chades, Iadine"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Bayesian Optimisation for Mixed-Variable Inputs using Value Proposals.

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Bayesian Optimisation for Mixed-Variable Inputs using Value Proposals.