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Boosting Gradient Ascent for Continuous DR-submodular Maximization
The fundamental idea of our boosting technique is to exploit non-oblivious search to derive a novel auxiliary function $F$, whose stationary points are excellent approximations to the global maximum of the original DR-submodular objective $f$.
Bandit Multi-linear DR-Submodular Maximization and Its Applications on Adversarial Submodular Bandits
Then we reduce submodular bandit with partition matroid constraint and bandit sequential monotone maximization to the online bandit learning of the monotone multi-linear DR-submodular functions, attaining $O(T^{2/3}\log T)$ of $(1-1/e)$-regret in both problems, which improve the existing results.
Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets
In this paper, we study MAB and SLB with quantum reward oracles and propose quantum algorithms for both models with $O(\mbox{poly}(\log T))$ regrets, exponentially improving the dependence in terms of $T$.
Bounded Memory Adversarial Bandits with Composite Anonymous Delayed Feedback
Our lower bound works even when the loss sequence is oblivious but the delay is non-oblivious.
