Pradeep ravikumar thesis

Beyond computation, generalization is a cornerstone of stationary time series analysis. Stronger than good on-average performance, generalization guarantees that the expected performance of an estimator improves over every time step of a data stream. Consequently, low regret does not imply generalization.  It is, however, possible to replace pure regret with a hybrid objective that becomes a batch (instantaneous) excess risk when the data is sufficiently regular.  I propose to investigate whether a CFA would remain possible, or how a CFA in the pure regret setting can generalize, if not optimally.

Pradeep ravikumar thesis

pradeep ravikumar thesis

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