Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Wan, Zongqi"'
Symmetric submodular maximization is an important class of combinatorial optimization problems, including MAX-CUT on graphs and hyper-graphs. The state-of-the-art algorithm for the problem over general constraints has an approximation ratio of $0.432
Externí odkaz:
http://arxiv.org/abs/2406.14278
Projected Gradient Ascent (PGA) is the most commonly used optimization scheme in machine learning and operations research areas. Nevertheless, numerous studies and examples have shown that the PGA methods may fail to achieve the tight approximation r
Externí odkaz:
http://arxiv.org/abs/2401.08330
The competitive auction was first proposed by Goldberg, Hartline, and Wright. In their paper, they introduce the competitive analysis framework of online algorithm designing into the traditional revenue-maximizing auction design problem. While the co
Externí odkaz:
http://arxiv.org/abs/2309.15414
We investigate the online bandit learning of the monotone multi-linear DR-submodular functions, designing the algorithm $\mathtt{BanditMLSM}$ that attains $O(T^{2/3}\log T)$ of $(1-1/e)$-regret. Then we reduce submodular bandit with partition matroid
Externí odkaz:
http://arxiv.org/abs/2305.12402
Multi-arm bandit (MAB) and stochastic linear bandit (SLB) are important models in reinforcement learning, and it is well-known that classical algorithms for bandits with time horizon $T$ suffer $\Omega(\sqrt{T})$ regret. In this paper, we study MAB a
Externí odkaz:
http://arxiv.org/abs/2205.14988
We study the adversarial bandit problem with composite anonymous delayed feedback. In this setting, losses of an action are split into $d$ components, spreading over consecutive rounds after the action is chosen. And in each round, the algorithm obse
Externí odkaz:
http://arxiv.org/abs/2204.12764
Publikováno v:
Frontiers of Computer Science; Jul2025, Vol. 19 Issue 7, p1-12, 12p
