Online Budget Allocation with Censored Semi-Bandit Feedback

We study a stochastic budget-allocation problem over $K$ tasks. At each round $t$, the learner chooses an allocation $X_t\in {\Delta }_K$. Task $k$ succeeds with probability $F_k(X_{t,k})$, where $F_1,\dots ,F_K$ are nondecreasing *budget-to-success* curves, and upon success yields a random reward with unknown mean ${\mu }_k$. The learner observes which tasks succeed, and observes a task's reward *only* upon success (*censored* semi-bandit feedback). This model captures, for instance, splitting payments across crowdsourcing workers or distributing bids across simultaneous auctions, and subsumes stochastic multi-armed bandits and semi-bandits. We design an optimism-based algorithm that operates under censored semi-bandit feedback. Our main result is an instance-dependent bound showing that in *diminishing-returns* regimes, the regret of this algorithm scales polylogarithmically with the horizon $T$ without any *ad hoc* tuning. For general nondecreasing curves, we prove that the same algorithm (with the same tuning) achieves a worst-case regret upper bound of $\tilde{O}(K\sqrt{T})$. Finally, we establish a matching worst-case regret lower bound of $\Omega (K\sqrt{T})$ that holds even for *full-feedback* algorithms, highlighting the intrinsic hardness of our problem outside diminishing returns.