Reviews: Variance-Reduced Stochastic Gradient Descent on Streaming Data

This paper considers the problem of streaming stochastic optimization taking into account the arrival patterns of examples in time. Whereas the relevant previous work focuses on learning from a stream (i.e. in one pass over the data, with O(dimension) memory), this work attempts to utilize the time spent between the arrival of examples in order to revisit previously encountered examples. The problem description is novel and appealing, both in its possible practical relevance and as an interesting new model for consideration in algorithm analysis. However, there are central issues with the comparisons that the paper draws between algorithms, both conceptually and experimentally. Conceptually, it seems incorrect to say that STRSAGA is a streaming algorithm, and in turn in to repeatedly compare it to one (such as SSVRG), since STRSAGA's memory complexity actually grows linearly with the dataset size (in order to maintain its "effective sample set").