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-objective multi-fidelity Bayesian optimization with causal priors.arXiv preprint arXiv:2602.00788, 2026
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.