Sparse Linear Bandit

Sparse linear bandits address the challenge of efficiently learning optimal actions in high-dimensional settings where only a small subset of features significantly influence rewards. Current research focuses on developing algorithms that achieve low regret despite this sparsity, often employing techniques like Lasso regression, iterative hard thresholding, and adaptive confidence sets to efficiently identify and utilize relevant features, even under adversarial conditions or privacy constraints. This field is significant for its applications in personalized medicine, online advertising, and other areas involving sequential decision-making with high-dimensional data, offering improved efficiency and reduced computational burden compared to non-sparse methods.