Efficient Bandit

Efficient bandit algorithms aim to optimize sequential decision-making under uncertainty, minimizing cumulative or simple regret by strategically selecting actions based on observed rewards. Current research focuses on developing algorithms robust to high-dimensional data, model misspecification (including adversarial and noisy settings), and privacy constraints, often employing techniques like online Newton methods, iterative hard thresholding, and Bayesian approaches. These advancements are crucial for addressing real-world applications in personalized medicine, online advertising, and resource allocation where efficient learning from limited data is paramount.