"thompson sampling" Papers
21 papers found
Conference
Adaptive Variance Inflation in Thompson Sampling: Efficiency, Safety, Robustness, and Beyond
Feng Zhu, David Simchi-Levi
Contextual Thompson Sampling via Generation of Missing Data
Kelly W Zhang, Tianhui Cai, Hongseok Namkoong et al.
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling
Jasmine Bayrooti, Carl Ek, Amanda Prorok
FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
Hong Huang, Jinhai Yang, Yuan Chen et al.
Feel-Good Thompson Sampling for Contextual Bandits: a Markov Chain Monte Carlo Showdown
Emile Anand, Sarah Liaw
Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions
Marc Brooks, Gabriel Durham, Kihyuk Hong et al.
Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear Bandits
Yuwei Luo, Mohsen Bayati
LaRes: Evolutionary Reinforcement Learning with LLM-based Adaptive Reward Search
Pengyi Li, Hongyao Tang, Jinbin Qiao et al.
No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes
Jasmine Bayrooti, Sattar Vakili, Amanda Prorok et al.
Thompson Sampling in Function Spaces via Neural Operators
Rafael Oliveira, Xuesong Wang, Kian Ming Chai et al.
Variance-Aware Feel-Good Thompson Sampling for Contextual Bandits
Xuheng Li, Quanquan Gu
$\mathtt{VITS}$ : Variational Inference Thompson Sampling for contextual bandits
Pierre Clavier, Tom Huix, Alain Oliviero Durmus
A Bayesian Approach to Online Planning
Nir Greshler, David Ben Eli, Carmel Rabinovitz et al.
Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search
Thomy Phan, Taoan Huang, Bistra Dilkina et al.
Efficient Exploration for LLMs
Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao et al.
Feel-Good Thompson Sampling for Contextual Dueling Bandits
Xuheng Li, Heyang Zhao, Quanquan Gu
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs
Tianyuan Jin, Hao-Lun Hsu, William Chang et al.
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
Shion Takeno, Yu Inatsu, Masayuki Karasuyama et al.
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
Andrew Jesson, Christopher Lu, Gunshi Gupta et al.
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models
Jongyeong Lee, Chao-Kai Chiang, Masashi Sugiyama
Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints
Yuantong Li, Guang Cheng, Xiaowu Dai