From Batch to Transductive Online Learning

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

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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.