Author

Zheng-Jia Zhuo

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Preprint Oct 2026

Near-Optimal Regret for Semi-Bandit Learning of Monotone Stochastic Problems

We consider the task of learning an (approximately) optimal policy for stochastic optimization problems where the true distributions are unknown. In each period, the algorithm plays a policy for the stochastic problem and observes semi-bandit feedback, i.e., it only receives new samples from the random variables that w...

Zheng-Jia Zhuo, Viswanath Nagarajan · 1 citation

Oct 2026

Semibandit Learning for Monotone Stochastic Optimization

Firms can improve decisions as data arrive without pausing operations for a separate data-gathering phase, and even with partial, censored, or yes-or-no feedback, the cumulative performance gap grows essentially at the square root rate.

Rohan Ghuge, Zheng-Jia Zhuo, Arpit Agarwal et al. · Operational Research · 2 citations · ⚡1