Ambiguity Set

Ambiguity sets represent a collection of probability distributions considered plausible for a given problem, addressing the challenge of decision-making under uncertainty where the true data-generating process is unknown. Current research focuses on developing robust optimization methods, often employing Bayesian techniques or Q-learning algorithms, to find optimal solutions that perform well across the entire ambiguity set, with particular attention paid to the choice of distance metric (e.g., Wasserstein, Kullback-Leibler) defining the set's boundaries. This work has significant implications for various fields, improving the robustness and reliability of machine learning models, control systems, and decision-making processes in the face of uncertainty and noisy data.