Universally Consistent Online Learning with Arbitrarily Dependent Responses
Proceedings of The 33rd International Conference on Algorithmic Learning Theory, PMLR 167:488-497, 2022.
This work provides an online learning rule that is universally consistent under processes on (X,Y) pairs, under conditions only on the X process. As a special case, the conditions admit all processes on (X,Y) such that the process on X is stationary. This generalizes past results which required stationarity for the joint process on (X,Y), and additionally required this process to be ergodic. In particular, this means that ergodicity is superfluous for the purpose of universally consistent online learning.