Author

T. Nguyen

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#machine learning Preprint Oct 2026

Halve-Then-Greedy: Adaptive Sampling for Posterior Success with Near-Optimal Instance Dependence

We study fixed-budget adaptive experimentation aimed at maximizing the probability of obtaining sufficient posterior evidence that a recommended design's mean performance exceeds a target. Unlike best-arm identification, this objective does not require finding the best design. For Gaussian bandits with a Gaussian prior...

Yi-Nan Li, Kwang-Sung Jun, Kapilan Balagopalan et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Bandits in Prod: Hyperparameter Optimization at Inference Time

This work introduces IMABO, a general framework that combines any bandit policy for choosing among already sampled configurations with any oracle for proposing new ones, and proves an expected cumulative quantile-regret bound of O(p_\rho^{-1/\beta} + T^{(1+\beta)/2})$.

L. Abraham, T. Nguyen, Nicolas Devatine · 0 citations