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arXiv · 2606.12763

Adaptive Weighted Averaging

Aditya Bhaskara·Ashok Cutkosky·Ravi Kumar·Manish Purohit
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Abstract

We study the problem of selecting the largest among $n$ unknown values $x_1,\dots,x_n$ given only a single unbiased estimate $y_i$ for each $x_i$. We design strategies that are simultaneously admissible (not uniformly dominated by any other strategy) and also never worse than a given baseline such as uniform random selection. We provide an application to stochastic optimization, where we obtain online-to-batch conversion bounds with a desirable "no-compromise" guarantee: they are never worse than standard random iterate selection, and yet can be significantly better in benign settings.

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Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit. 2026-06-11. Adaptive Weighted Averaging. https://arxiv.org/abs/2606.12763

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