Adversarial Resilience of Poisson-Process Submodular Maximization over Matroids, and Full-Bandit Learning
We study nonnegative submodular maximization on $n$ elements subject to a general matroid of rank $k$, when the offline algorithm is given an arbitrary controlled value oracle. Our main result is an adversarial resilience theorem for the Spiteful Greedy Swap Poisson Process (SGS-Poisson): without modifying its Poisson intensity, single-element exchange rule, or spiteful drop step, the algorithm retains limiting approximation factors $1/e$ for non-monotone objectives and $1-1/e$ for monotone objectives. More precisely, given an error bound $ξ\ge0$, under every controlled oracle $\widehat f$ satisfying $|\widehat f(S)-f(S)|\le ξ$ for every set $S$, our implementation returns a feasible set ...
| Authors | Vaneet Aggarwal |
|---|---|
| Primary category | cs.LG |
| Published | 12 August 2026 |
| Last updated at source | 7 September 2026 |
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