Adaptive Bayesian distributionally robust optimal control with tractable reformulation, infinite-horizon consistency, finite-sample credibility guarantees, and a convergent Bellman-operator cutting-plane algorithm.
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Presents the first kernel framework for distributional treatment effect inference from adaptively collected data, using doubly robust RKHS scores, cross-fold witness functions, and sequentially normalized statistics with valid type-I error.
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Adaptive Distributionally Robust Optimal Control with Bayesian Ambiguity Sets
Adaptive Bayesian distributionally robust optimal control with tractable reformulation, infinite-horizon consistency, finite-sample credibility guarantees, and a convergent Bellman-operator cutting-plane algorithm.
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Kernel Treatment Effects with Adaptively Collected Data
Presents the first kernel framework for distributional treatment effect inference from adaptively collected data, using doubly robust RKHS scores, cross-fold witness functions, and sequentially normalized statistics with valid type-I error.