arXiv:2502. 13467v2 Announce Type: replace Abstract: The $K$-Max combinatorial multi-armed bandit problem arises in applications such as recommendation and distributed decision making, where the reward is determined by the maximum outcome among $K$ selected arms.
By Yu Chen, Siwei Wang, Longbo Huang, Wei Chen
arXiv:2608.24627v1 Announce Type: new
Abstract: We study adversarial bandit maximization of monotone submodular functions under a matroid constraint. For a rank-$k$ matroid on $n$ elements, we give a...
By Zongqi Wan, Zhijie Zhang
arXiv:2602. 20578v2 Announce Type: replace Abstract: We study online maximization of non-monotone Diminishing-Return(DR)-submodular functions over down-closed convex sets, a regime where existing projection-free online methods suffer from suboptimal regret and limited feedback guarantees.
By Yiyang Lu, Haresh Jadav, Mohammad Pedramfar, Ranveer Singh, Vaneet Aggarwal
arXiv:2609.26213v1 Announce Type: cross
Abstract: We study the multiplayer multi-armed bandit problem with information asymmetry under Bernoulli rewards, for three information structures: asymmetry i...
By Khang Nguyen, Ricardo Parada, William Chang
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
arXiv:2312. 15427v3 Announce Type: replace Abstract: Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables.
By Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan, Zhengjia Zhuo