@report {1617112,
	title = {From Batch to Transductive Online Learning},
	year = {2006},
	address = {Advances in Neural Information Processing Systems 18 (NIPS 2005)},
	abstract = {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.},
	url = {https://papers.nips.cc/paper/2005/hash/17693c91d9204b7a7646284bb3adb603-Abstract.html},
	author = {Sham M. Kakade and Adam Kalai}
}
