Sham Kakade, Adam Tauman Kalai
It is well-known that everything that is learnable in the difﬁcult online setting, where an arbitrary sequences of examples must be labeled one at a time, is also learnable in the batch setting, where examples are drawn independently from a distribution. We show a result in the opposite di- rection. We give an efﬁcient conversion algorithm from batch to online that is transductive: it uses future unlabeled data. This demonstrates the equivalence between what is properly and efﬁciently learnable in a batch model and a transductive online model.