Contextual Bandits K actions/classes Rewards depends on context x: μ(x) For t = 1…T – Algorithm receives context x t – Algorithm chooses action a(t) – Receives random reward y(t) Expectation μ(x t ) Goal: Minimize Regret Lecture 17: The Multi-Armed Bandit Problem46 K classes Best class depends on features Algorithm Simultaneously Predicts & Receives Labels Bandit multiclass prediction http://arxiv.org/abs/1402.0555 http://www.research.rutgers.edu/~lihong/pub/Li10Contextual.pdf
{}