论文标题
开放问题：上下文匪徒的模型选择
Open Problem: Model Selection for Contextual Bandits
论文作者
论文摘要
在统计学习中，用于模型选择的算法使学习者可以适应顺序中最佳假设类的复杂性。我们询问是否可以进行上下文匪徒学习的类似保证。
In statistical learning, algorithms for model selection allow the learner to adapt to the complexity of the best hypothesis class in a sequence. We ask whether similar guarantees are possible for contextual bandit learning.
