Results 41 to 50 of about 2,925,819 (300)
Thompson sampling with the online bootstrap
CoRR, 2014 Thompson sampling provides a solution to bandit problems in which new observations are allocated to arms with the posterior probability that an arm is optimal. While sometimes easy to implement and asymptotically optimal, Thompson sampling can be computationally demanding in large scale bandit problems, and its performance is dependent on the model fit
Dean Eckles, Maurits Kaptein
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Mixed-Effect Thompson Sampling
, 2022 A contextual bandit is a popular framework for online learning to act under uncertainty. In practice, the number of actions is huge and their expected rewards are correlated. In this work, we introduce a general framework for capturing such correlations through a mixed-effect model where actions are related through multiple shared effect parameters. To
Imad Aouali +2 more
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Austrian Journal of Statistics, 2021 Strong laws of large numbers with arbitrary norming sequences for nonnegative not necessarily independent random variables are obtained. From these results we establish, among other things, stability results for weighted sums of nonnegative random ...
Tobias Schoch
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Thompson Sampling on Asymmetric a-stable Bandits
Proceedings of the 15th International Conference on Agents and Artificial Intelligence, 2023 8 pages, 4 ...
Zhendong Shi +2 more
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Sample-Then-Optimize Batch Neural Thompson Sampling
Advances in Neural Information Processing Systems 35, 2022 Accepted to 36th Conference on Neural Information Processing Systems (NeurIPS 2022), Extended version with proofs and additional experimental details and results, 30 ...
Zhongxiang Dai +3 more
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Research on Twitter User Tag Preference Prediction Based on Thompson Sampling Algorithm [PDF]
ITM Web of ConferencesTwitter's user behaviour data is crucial for studying user patterns and content recommendation. To achieve this goal, the paper first preprocesses a Twitter user dataset obtained from Kaggle.
Shi Yixuan
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Thompson Sampling for the MNL-Bandit
CoRR, 2017 Accepted for presentation at Conference on Learning Theory (COLT ...
Shipra Agrawal 0001 +3 more
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On the Prior Sensitivity of Thompson Sampling [PDF]
, 2016 Appears in the 27th International Conference on Algorithmic Learning Theory (ALT ...
Che-Yu Liu, Lihong Li 0001
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Asymptotic Convergence of Thompson Sampling
CoRR, 2020 Thompson sampling has been shown to be an effective policy across a variety of online learning tasks. Many works have analyzed the finite time performance of Thompson sampling, and proved that it achieves a sub-linear regret under a broad range of probabilistic settings. However its asymptotic behavior remains mostly underexplored.
Cem Kalkanli, Ayfer Özgür
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Diffuse MRI Edema Predicts Relapse in Cerebral Amyloid Angiopathy–Related Inflammation
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To identify MRI predictors of relapse and quantify relapse and mortality risk in cerebral amyloid angiopathy–related inflammation (CAA‐RI). A secondary objective was to assess the association between mycophenolate mofetil use and relapse risk. Methods We performed a retrospective cohort study of 36 patients with CAA‐RI treated at the
G. Abbas Kharal +10 more
wiley +1 more source