Graph-coupled causal Bayesian optimization couples intervention effects via shared causal parameters to produce a low-rank causal kernel, logarithmic information-gain bounds, and a regret bound separating optimization, estimation, and intervention-choice errors in linear Gaussian models.
Multi-fidelity Bayesian optimization in engineering design.arXiv preprint arXiv:2311.13050, 2023
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Transferring Information Across Interventions in Causal Bayesian Optimization
Graph-coupled causal Bayesian optimization couples intervention effects via shared causal parameters to produce a low-rank causal kernel, logarithmic information-gain bounds, and a regret bound separating optimization, estimation, and intervention-choice errors in linear Gaussian models.