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Learning Causal Bayesian Networks from Observations and Experiments: A Decision Theoretic Approach.

, , and . MDAI, volume 3885 of Lecture Notes in Computer Science, page 58-69. Springer, (2006)

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Inference in multi-agent causal models., , and . Int. J. Approx. Reasoning, 46 (2): 274-299 (2007)Causal Graphical Models with Latent Variables: Learning and Inference., , , and . Innovations in Bayesian Networks, volume 156 of Studies in Computational Intelligence, Springer, (2008)BioLMiner System: Interaction Normalization Task and Interaction Pair Task in the BioCreative II.5 Challenge., , and . IEEE/ACM Trans. Comput. Biology Bioinform., 7 (3): 428-441 (2010)The scalarized multi-objective multi-armed bandit problem: An empirical study of its exploration vs. exploitation tradeoff., , and . IJCNN, page 2290-2297. IEEE, (2014)Knowledge Gradient for Multi-objective Multi-armed Bandit Algorithms., , and . ICAART (1), page 74-83. SciTePress, (2014)Pareto Upper Confidence Bounds algorithms: An empirical study., , and . ADPRL, page 1-8. IEEE, (2014)Learning Semi-Markovian Causal Models using Experiments., , , and . Probabilistic Graphical Models, page 195-206. (2006)Finding Optimal Representations Using the Crossover Correlation Coefficient., and . SBIA, volume 1159 of Lecture Notes in Computer Science, page 91-100. Springer, (1996)Evolvable Hardware: An Outlook., and . ICES, volume 1259 of Lecture Notes in Computer Science, page 305-311. Springer, (1996)Adaptiveness of CMA based samplers., , , and . GECCO (Companion), page 179-180. ACM, (2017)