arXiv Machine Learning

Online Bipartite Matching with Reusable Capacity under Non-Stationary Rewards

arXiv:2608. 18130v1 Announce Type: cross Abstract: We study online bipartite matching with reusable server capacity and non-stationary rewards.

arXiv AI
Sep 3

Online Non-Monotone DR-Submodular Maximization Matching the Offline $0.401$ Factor

The paper presents an online algorithm that achieves the same $0.401$ approximation factor for maximizing nonnegative, non-monotone DR-submodular functions over compact convex down-closed subsets of the $d$-dimensional unit cube as the best known offline construction. In the full-information value-oracle model, the algorithm attains this factor with sublinear regret, using $O(dT^{1/4})$ oracle calls per round and $O(T^{3/4})$ regret, and offers flexible batching trade-offs. Under a positive-anchor condition, a randomized blocking strategy preserves the $0.401$ factor while achieving $O(T^{5/6})$ one-point bandit regret.

By Vaneet Aggarwal, Yiyang Lu
arXiv AI
Jul 14

Efficient Online Proportional Sampling with Applications to Smoothed Online Learning

arXiv:2607. 10963v1 Announce Type: cross Abstract: We study the problem of efficient online proportional sampling from a high-dimensional domain under a $\sigma$-smoothed adversary, where the sampling distribution is induced by a dynamically evolving weight function defined over a sequence of piecewise-structured partitions.

By Amirmahdi Mirfakhar, Maria-Florina Balcan, Hedyeh Beyhaghi