Transfer Learning for Contextual Multi-armed Bandits
Motivated by a range of applications, we study in this paper the problem of transfer learning for nonparametric contextual multi-armed bandits under the covariate shift model, where we have data collected on source bandits before the start of the target bandit learning. The minimax rate of convergence for the cumulative regret is established and a novel transfer learning algorithm that attains the minimax regret is proposed. The results quantify the contribution of the data from the source domains for learning in the target domain in the context of nonparametric contextual multi-armed bandits. In view of the general impossibility of adaptation to unknown smoothness, we develop a data-driven algorithm that achieves near-optimal statistical guarantees (up to a logarithmic factor) while automatically adapting to the unknown parameters over a large collection of parameter spaces under an additional self-similarity assumption. A simulation study is carried out to illustrate the benefits of utilizing the data from the auxiliary source domains for learning in the target domain.
Code (0)
등록된 구현이 없습니다.
Tasks
Multi-Armed BanditsTransfer LearningSimilar Papers 제목 키워드 기반
Deep Contextual Multi-armed Bandits
Contextual multi-armed bandit problems arise frequently in important industrial applications. Existing solutions model the context either linearly, which enables uncertainty driven (principled) exploration, or non-linear…
MarketingMulti-Armed BanditsThompson SamplingOSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits
We consider the stochastic linear (multi-armed) contextual bandit problem with the possibility of hidden simple multi-armed bandit structure in which the rewards are independent of the contextual information. Algorithms …
Multi-Armed BanditsAnalysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits
Contextual multi-armed bandits are classical models in reinforcement learning for sequential decision-making associated with individual information. A widely-used policy for bandits is Thompson Sampling, where samples fr…
Decision MakingMulti-Armed Banditsreinforcement-learningReinforcement Learning (RL)+2Adapting multi-armed bandits policies to contextual bandits scenarios
This work explores adaptations of successful multi-armed bandits policies to the online contextual bandits scenario with binary rewards using binary classification algorithms such as logistic regression as black-box orac…
Binary ClassificationClassificationGeneral ClassificationMulti-Armed Bandits+2Jump Starting Bandits with LLM-Generated Prior Knowledge
We present substantial evidence demonstrating the benefits of integrating Large Language Models (LLMs) with a Contextual Multi-Armed Bandit framework. Contextual bandits have been widely used in recommendation systems to…
Multi-Armed BanditsRecommendation Systems