Learning with little mixing. (arXiv:2206.08269v1 [cs.LG])

Web: http://arxiv.org/abs/2206.08269

We study square loss in a realizable time-series framework with martingale
difference noise. Our main result is a fast rate excess risk bound which shows
that whenever a trajectory hypercontractivity condition holds, the risk of the
least-squares estimator on dependent data matches the iid rate order-wise after
a burn-in time. In comparison, many existing results in learning from dependent
data have rates where the effective sample size is deflated by a factor of the
mixing-time of the underlying process, even …

arxiv learning lg