Results 21 to 30 of about 2,925,819 (300)
Thompson Sampling with Unrestricted Delays
Proceedings of the 23rd ACM Conference on Economics and Computation, 2022 We investigate properties of Thompson Sampling in the stochastic multi-armed bandit problem with delayed feedback. In a setting with i.i.d delays, we establish to our knowledge the first regret bounds for Thompson Sampling with arbitrary delay distributions, including ones with unbounded expectation.
Han Wu, Stefan Wager
openaire +3 more sources
Variable Selection Via Thompson Sampling [PDF]
Journal of the American Statistical Association, 2021 Thompson sampling is a heuristic algorithm for the multi-armed bandit problem which has a long tradition in machine learning. The algorithm has a Bayesian spirit in the sense that it selects arms based on posterior samples of reward probabilities of each arm.
Yi Liu 0072, Veronika Rocková
openaire +3 more sources
Proceedings of the AAAI Conference on Artificial Intelligence, 2014 Thompson Sampling (TS) has surged a lot of interest due to its good empirical performance, in particular in the computational advertising. Though successful, the tools for its performance analysis appeared only recently. In this paper, we describe and analyze SpectralTS algorithm for a bandit problem, where the payoffs of the choices ...
Kocák, Tomáš +3 more
openaire +5 more sources
An Asymptotic Variance Formula for Sampling Without Replacement with Unequal Probabilities [PDF]
The Egyptian Statistical Journal, 1972 Asymptotic formulae for the variance of the Horvitz and Thompson Estimator for sampling with unequal probabilities without replacement have e inter previously been derived for a number of particular sampling procedures.
Muhammad Hanif
doaj +1 more source
Application of Dynamic Multi-Armed Bandit in Real-Time System: Comparative Study of Thompson Sampling and Ucb Algorithm [PDF]
ITM Web of ConferencesThis study compares the performance of Thompson Sampling (TS) and Upper Confidence Bound (UCB) algorithms under the framework of dynamic multi-arm slot machines.
Yu Jiangzi
doaj +1 more source
On Thompson Sampling with Langevin Algorithms
CoRR, 2020 Thompson sampling for multi-armed bandit problems is known to enjoy favorable performance in both theory and practice. However, it suffers from a significant limitation computationally, arising from the need for samples from posterior distributions at every iteration.
Mazumdar, Eric +4 more
openaire +3 more sources
Thompson Sampling for Unimodal Bandits
CoRR, 2021 In this paper, we propose a Thompson Sampling algorithm for \emph{unimodal} bandits, where the expected reward is unimodal over the partially ordered arms. To exploit the unimodal structure better, at each step, instead of exploration from the entire decision space, our algorithm makes decision according to posterior distribution only in the ...
Yang, Long +6 more
openaire +2 more sources
Adaptive Android Program Test Method Based on Thompson Sampling [PDF]
Jisuanji kexueRecently,the research of Android graphical interface test method has attracted wide attention.At present,most test methods are developed based on reinforcement learning.However,the existing methods can explore the application by selecting parameters ...
ZHAO Yingnan, LENG Chongyang, HAN Qilong, YU Cheng
doaj +1 more source
Optimizing Click-Through Rates in Online Advertising Using Thompson Sampling [PDF]
ITM Web of ConferencesAdvertisers must optimize ad selection to increase click-through rates (CTR) in an unpredictable environment as a result of the quick expansion of online advertising.
Zhao Ben
doaj +1 more source
New Randomized Response Technique for Estimating the Population Total of a Quantitative Variable [PDF]
Statistika: Statistics and Economy Journal, 2022 A new randomized response technique for estimating the population total, or the population mean of a quantitative variable is proposed. It provides a high degree of protection to the respondents because they never report their data.
Jaromír Antoch +2 more
doaj +1 more source