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Early stopping is a well known approach to reduce the time complexity forperforming training and model selection of large scale learning machines. Onthe other hand, memory/space (rather than time) complexity is the mainconstraint in many applications, and randomized subsampling techniques havebeen proposed to tackle this issue. In this paper we ask whether early stoppingand subsampling ideas can be combined in a fruitful way. We consider thequestion in a least squares regression setting and propose a form of randomizediterative regularization based on early stopping and subsampling. In thiscontext, we analyze the statistical and computational properties of theproposed method. Theoretical results are complemented and validated by athorough experimental analysis.

NYTRO: When Subsampling Meets Early Stopping