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Review for NeurIPS paper: Stochastic Optimization with Laggard Data Pipelines

Neural Information Processing Systems

Clarity: The paper writing is very good, but I find several small problems related to notations, which could make confusion: - Between line 108-109, the authors use both the \bf\xi with a supscript "t" and the \bf\xi without a supscript "t", I guess for the latter the authors mean a general batch of samples does not depend on "t", but it is not explained clearly. Also, sometimes it has "i" in the supscript while othertimes it has "i" in the subscript. However, the reuse of the same notation really makes me confused for a while since it looks like \xi is some element belong to \bf\xi or \bf\xi'. Is this a proof artifact? It makes more sense that if we want to do an averaging here, the w_t's should better have different weights such that the recent updates get higher score.