A tractable ensemble distributionally robust Bayesian optimization method achieves improved sublinear regret bounds under context uncertainty.
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doi:10.1103/physrevd.97.014021 , url =
2Pith papers citing it
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cs.LGtop field · 2 papers
UNVERDICTEDtop verdict bucket · 2 papers
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Pith has found this work in 2 reviewed papers. Its strongest current cluster is cs.LG (2 papers). The largest review-status bucket among citing papers is UNVERDICTED (2 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
BRICKS creates compositional neural Markov kernels via hybrid transformers and Riemannian Flow Matching on product manifolds to enable zero-shot simulation of radiation-matter interactions across arbitrary material distributions.
citing papers explorer
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Ensemble Distributionally Robust Bayesian Optimisation
A tractable ensemble distributionally robust Bayesian optimization method achieves improved sublinear regret bounds under context uncertainty.
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BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation
BRICKS creates compositional neural Markov kernels via hybrid transformers and Riemannian Flow Matching on product manifolds to enable zero-shot simulation of radiation-matter interactions across arbitrary material distributions.