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Found 8 papers, 1 papers with code
Bandit-supported care planning for older people with complex health and care needs
Long-term care service for old people is in great demand in most of the aging societies.
GBOSE: Generalized Bandit Orthogonalized Semiparametric Estimation
In this work we propose a new algorithm with a semi-parametric reward model with state-of-the-art complexity of upper bound on regret amongst existing semi-parametric algorithms.
Robust Tests in Online Decision-Making
In this work, we propose a modified actor-critic algorithm which is robust to critic misspecification and derive a novel testing procedure for the actor parameters in this case.
Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization
Non-stationarity is ubiquitous in human behavior and addressing it in the contextual bandits is challenging.
Doubly Robust Thompson Sampling with Linear Payoffs
A challenging aspect of the bandit problem is that a stochastic reward is observed only for the chosen arm and the rewards of other arms remain missing.
Doubly robust Thompson sampling for linear payoffs
A challenging aspect of the bandit problem is that a stochastic reward is observed only for the chosen arm and the rewards of other arms remain missing.
Doubly-Robust Lasso Bandit
Contextual multi-armed bandit algorithms are widely used in sequential decision tasks such as news article recommendation systems, web page ad placement algorithms, and mobile health.
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model
We prove that the high-probability upper bound of the regret incurred by the proposed algorithm has the same order as the Thompson sampling algorithm for linear reward models.
