Auto-regressive models, trained simply to predict future observations from past ones, have become a default tool for sequential data, from language to video to control-relevant time series. This talk first addresses that gap in a canonical setting and then concludes with a discussion of a class systems which captures these observer effects, recent results on learning and control, and several open questions.
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