Robust Satisficing

Robust satisficing is a novel approach to optimization that aims to find solutions exceeding a minimum acceptable threshold, while being robust to uncertainties in the underlying data distribution. Current research focuses on developing efficient algorithms, such as those based on Kullback-Leibler divergence and Bayesian methods, and analyzing their statistical properties, including generalization error bounds and confidence intervals. This framework offers advantages over traditional methods like empirical risk minimization, particularly in scenarios with limited data or distribution shifts, showing promise for improved performance and reduced computational cost in various machine learning applications, including deep reinforcement learning and contextual bandit problems.