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Spatio-temporal Bayesian optimization for point-process data is now scalable and efficient, achieving accurate peak discovery with minimal evaluations.
Amortized Bayesian causal inference for hidden confounding is now possible via a single transformer pass, achieving state-of-the-art CATE estimation without hyperparameter tuning.
