EDRBO uses ensemble surrogates and Wasserstein ambiguity sets to robustify BO acquisition functions against context distribution mismatch, with sublinear regret O(γ_T √T) and SOTA empirical results on continuous contexts.
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Graph neural networks can identify and remove unwanted beam background depositions in the Belle II calorimeter to improve hadronic clustering and reduce fake photon clusters.
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Ensemble Distributionally Robust Bayesian Optimisation with Continuous Context
EDRBO uses ensemble surrogates and Wasserstein ambiguity sets to robustify BO acquisition functions against context distribution mismatch, with sublinear regret O(γ_T √T) and SOTA empirical results on continuous contexts.
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Using Graph Neural Networks for hadronic clustering and to reduce beam background in the Belle~II electromagnetic calorimeter
Graph neural networks can identify and remove unwanted beam background depositions in the Belle II calorimeter to improve hadronic clustering and reduce fake photon clusters.