Adversarial Multi-armed Bandit for mmWave Beam Alignment with One-Bit Feedbackdoi:10.1145/3306309.3306315Irched ChafaaE. Veronica BelmegaMérouane DebbahACMPerformance Evaluation Methodolgies and Tools
Regret Analysis of Stochastic and Non-stochastic Multi-armed Bandit Problems, Sebastien Bubeck. 1 问题描述 在Adversarial bandit problem 中, 我们不再假设 reward 是从一个固定的分布中采样获得, 相反, 它由一个称为adversary 的 环境来决定. Adversarial bandit problem: ...
This is the adversarial problem. 这就是对抗性问题。 权威例句 Generative adversarial nets Generative Adversarial Nets Recognizing Objects in Adversarial Clutter: Breaking a Visual CAPTCHA Gambling in a rigged casino: The adversarial multi-armed bandit problem ...
We fill in a long open gap in the characterization of the minimax rate for the multi-armed bandit problem. Concretely, we remove an extraneous logarithmic factor in the previously known upper bound and propose a new family of randomized algorithms based on an implicit normalization, as well as...
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We study the adversarial multi-armed bandit problem, in which a player must iteratively make online decisions with linear loss vectors and hopes to achieve a small total loss. We consider a natural measure on the loss vectors, called deviation, which is the sum of the distances between every ...
E. Schapire., "Gambling in a rigged casino: The adversarial multi-armed bandit problem", 36th Annual Symposium on Foundations of Computer Science, November, 1995.P. Auer, N. Cesa-Bianchi, Y. Freund, and R. E. Schapire. Gambling in a rigged casino: The adver- sarial multi-armed bandit ...
We investigate the optimality of perturbation based algorithms in the stochastic and adversarial multi-armed bandit problems. For the stochastic case, we provide a unified regret analysis for both sub-Weibull and bounded perturbations when rewards are sub-Gaussian. Our bounds are instance optimal for ...
Specifically, an adversarial multi-armed bandit (MAB) formalism is first proposed, whereby no prior knowledge about channel conditions is required and the reward sequences are not restrained by any statistical assumptions. Second, considering the curse of dimensionality caused by exponentially large ...
In this paper, we propose sequential learning-based algorithms based on multi-armed bandit (MAB) systems to deal with the node selection problem. Unlike previous MAB approaches, we contribute novel MAB algorithms for node selection using deep learning expert models. To tackle the inherent ...