Preprint
Nov 2026
Beyond Monotonicity: Stochastic Online Optimization under Semi-Bandit Feedback
We study stochastic online optimization (SOO) under semi-bandit feedback: in each of $T$ rounds, a policy adaptively observes $n$ independent random variables with unknown distributions, learning only from observed values. First-order stochastic dominance (FSD) monotonicity enables general reductions for learning SOO,...
Tsubasa Harada, Yasushi Kawase, Hanna Sumita
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