Results 1 to 10 of about 53,559 (154)
Monte Carlo Thompson sampling-guided design for antibody engineering [PDF]
mAbs, 2023 Antibodies are one of the predominant treatment modalities for various diseases. To improve the characteristics of a lead antibody, such as antigen-binding affinity and stability, we conducted comprehensive substitutions and exhaustively explored their ...
Taro Kakuzaki +8 more
doaj +2 more sources
Thompson Sampling for Non-Stationary Bandit Problems [PDF]
EntropyNon-stationary multi-armed bandit (MAB) problems have recently attracted extensive attention. We focus on the abruptly changing scenario where reward distributions remain constant for a certain period and change at unknown time steps.
Han Qi, Fei Guo, Li Zhu
doaj +2 more sources
A Tutorial on Thompson Sampling [PDF]
Foundations and Trends in Machine Learning, 2018 Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may improve future performance.
Daniel Russo, Benjamin van Roy
exaly +5 more sources
IntelligentPooling: Practical Thompson Sampling for mHealth. [PDF]
Mach Learn, 2021 In mobile health (mHealth) smart devices deliver behavioral treatments repeatedly over time to a user with the goal of helping the user adopt and maintain healthy behaviors. Reinforcement learning appears ideal for learning how to optimally make these sequential treatment decisions.
Tomkins S, Liao P, Klasnja P, Murphy S.
europepmc +5 more sources
Partial Likelihood Thompson Sampling
CoRR, 2022 We consider the problem of deciding how best to target and prioritize existing vaccines that may offer protection against new variants of an infectious disease. Sequential experiments are a promising approach; however, challenges due to delayed feedback and the overall ebb and flow of disease prevalence make available methods inapplicable for this task.
Wu H, Wager S.
europepmc +4 more sources
Enhanced Thompson sampling by roulette wheel selection for screening ultralarge combinatorial libraries [PDF]
Journal of CheminformaticsChemical space exploration has gained significant interest with the increasing availability of building blocks, enabling the creation of ultralarge virtual libraries containing billions or trillions of compounds.
Hongtao Zhao +5 more
doaj +2 more sources
Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis [PDF]
EntropyWe study stochastic linear contextual bandits (CB) where the agent observes a noisy version of the true context through a noise channel with unknown channel parameters. Our objective is to design an action policy that can “approximate” that of a Bayesian
Sharu Theresa Jose, Shana Moothedath
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Adaptive Probabilistic RREQ Rebroadcasting Using Thompson Sampling for Mobile Ad Hoc Sensor Networks [PDF]
SensorsRoute discovery in ad hoc on-demand distance vector (AODV)-based mobile ad hoc sensor networks relies on route request (RREQ) dissemination, which improves reachability, but generates redundant rebroadcasts, channel contention, delay, and energy waste ...
Dimitra G. Kampitaki +1 more
doaj +2 more sources
Linear Thompson Sampling Revisited
Electronic Journal of Statistics, 2017 We derive an alternative proof for the regret of Thompson sampling (\ts) in the stochastic linear bandit setting. While we obtain a regret bound of order $\widetilde{O}(d^{3/2}\sqrt{T})$ as in previous results, the proof sheds new light on the functioning of the \ts.
Alessandro Lazaric
exaly +6 more sources
Personalized Dynamic Pricing Based on Improved Thompson Sampling
MathematicsThis study investigates personalized pricing with demand learning. We first encode consumer-personalized feature information into high-dimensional vectors, then establish the relationship between this feature vector and product demand using a logit model,
Wenjie Bi, Bing Wang, Haiying Liu
doaj +3 more sources