Sequential Probability Assignment

Sequential probability assignment, also known as online learning with logarithmic loss, focuses on predicting a sequence of events while minimizing cumulative prediction error. Current research emphasizes deriving tight bounds on the minimax regret, a measure of worst-case performance compared to the best predictor in a given class, and developing algorithms like contextual normalized maximum likelihood (cNML) and truncated Bayesian approaches that achieve these bounds. This field is crucial for advancing online learning algorithms and has implications for various applications, including reinforcement learning and robust decision-making under uncertainty, particularly in scenarios with limited data or changing environments.