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 ...
This indicates that the sleeping problem with preference feedback is inherently more difficult than that for classical multi-armed bandits (MAB). We then propose two algorithms, with near-optimal regret guarantees. All in all, we present a clean tradeoff between regret-vs-availability sequence i...
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...
Code Edit No code implementations yet. Submit your code now Tasks Edit Management Multi-Armed Bandits Datasets Edit Add Datasets introduced or used in this paper Results from the Paper Edit Submit results from this paper to get state-of-the-art GitHub badges and help the community ...
This paper explores the application of bandit algorithms in both stochastic and adversarial settings, with a focus on theoretical analysis and practical applications. The study begins by introducing bandit problems, distinguishing between stochastic and adversarial variants, and examining key algorithms such...
Adversarial Multi-armed Bandit for mmWave Beam Alignment with One-Bit Feedbackdoi:10.1145/3306309.3306315Irched ChafaaE. Veronica BelmegaMérouane DebbahACMPerformance Evaluation Methodolgies and Tools
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 ...
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 ...
Schapire. Gambling in a rigged casino: The adversarial multi-armed bandit problem. In Pro- ceedings of the 36th Annual IEEE Symposium on Foundations of Computer Science, 1995.Peter Auer, Nicolo` Cesa-Bianchi, Yoav Freund, and Robert E. Schapire. Gambling in a rigged casino: the adversarial ...