Online Learning: Stochastic and Constrained Adversaries

@article{Rakhlin2011OnlineLS,
  title={Online Learning: Stochastic and Constrained Adversaries},
  author={Alexander Rakhlin and Karthik Sridharan and Ambuj Tewari},
  journal={CoRR},
  year={2011},
  volume={abs/1104.5070}
}
Learning theory has largely focused on two main learning scenarios. The first is the classical statistical setting where instances are drawn i.i.d. from a fixed distribution and the second scenario is the online learning, completely adversarial scenario where adversary at every time step picks the worst instance to provide the learner with. It can be argued that in the real world neither of these assumptions are reasonable. It is therefore important to study problems with a range of assumptions… CONTINUE READING