Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Hierarchical bayesian bandits
Abstract Meta-, multi-task, and federated learning can be all viewed as solving similar tasks,
drawn from a distribution that reflects task similarities. We provide a unified view of all these …
drawn from a distribution that reflects task similarities. We provide a unified view of all these …
[PDF][PDF] Adaptivity and confounding in multi-armed bandit experiments
We explore a new model of bandit experiments where a potentially nonstationary sequence
of contexts influences arms' performance. Context-unaware algorithms risk confounding …
of contexts influences arms' performance. Context-unaware algorithms risk confounding …
Mixed-effect thompson sampling
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 …
practice, the number of actions is huge and their expected rewards are correlated. In this …
Multi-task off-policy learning from bandit feedback
Many practical problems involve solving similar tasks. In recommender systems, the tasks
can be users with similar preferences; in search engines, the tasks can be items with similar …
can be users with similar preferences; in search engines, the tasks can be items with similar …
Transportability for bandits with data from different environments
A Bellot, A Malek, S Chiappa - Advances in Neural …, 2023 - proceedings.neurips.cc
A unifying theme in the design of intelligent agents is to efficiently optimize a policy based on
what prior knowledge of the problem is available and what actions can be taken to learn …
what prior knowledge of the problem is available and what actions can be taken to learn …
Lifelong bandit optimization: no prior and no regret
Abstract Machine learning algorithms are often repeatedly. applied to problems with similar
structure over and over again. We focus on solving a sequence of bandit optimization tasks …
structure over and over again. We focus on solving a sequence of bandit optimization tasks …
Meta Learning in Bandits within shared affine Subspaces
We study the problem of meta-learning several contextual stochastic bandits tasks by
leveraging their concentration around a low dimensional affine subspace, which we learn …
leveraging their concentration around a low dimensional affine subspace, which we learn …
Thompson sampling with diffusion generative prior
In this work, we initiate the idea of using denoising diffusion models to learn priors for online
decision making problems. Our special focus is on the meta-learning for bandit framework …
decision making problems. Our special focus is on the meta-learning for bandit framework …
Thompson sampling for robust transfer in multi-task bandits
We study the problem of online multi-task learning where the tasks are performed within
similar but not necessarily identical multi-armed bandit environments. In particular, we study …
similar but not necessarily identical multi-armed bandit environments. In particular, we study …
Prior-dependent allocations for bayesian fixed-budget best-arm identification in structured bandits
We study the problem of Bayesian fixed-budget best-arm identification (BAI) in structured
bandits. We propose an algorithm that uses fixed allocations based on the prior information …
bandits. We propose an algorithm that uses fixed allocations based on the prior information …