Sequential Probability Assignment with Contexts: Characterizing Minimax Regret and the Optimal Algorithm

This paper introduces the "contextual Shtarkov sum," a novel complexity measure that characterizes the minimax regret in sequential probability assignment with contexts. Furthermore, it presents "contextual Normalized Maximum Likelihood" (cNML), a minimax optimal algorithm derived from this measure.