Oracle-Efficient Combinatorial Semi-Bandits

We study the combinatorial semi-bandit problem where an agent selects a subset of base arms and receives individual feedback. While this generalizes the classical multi-armed bandit and has broad applicability, its scalability is limited by the high cost of combinatorial optimization, requiring oracle queries at every round.

To tackle this, we propose oracle-efficient frameworks that significantly reduce oracle calls while maintaining tight regret guarantees. For worst-case linear rewards, our algorithms achieve $\tilde{O}(\sqrt{T})$ regret using only $O(\log \log T)$ oracle queries. We also propose covariance-adaptive algorithms that leverage noise structure for improved regret, and extend our approach to general (non-linear) rewards.

Overall, our methods reduce oracle usage from linear to (doubly) logarithmic in time, with strong theoretical guarantees.