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Without-Replacement Sampling for Stochastic Gradient Methods
Ohad Shamir

Wed Dec 07 07:20 AM -- 07:40 AM (PST) @ Area 3
in Optimization »

Stochastic gradient methods for machine learning and optimization problems are usually analyzed assuming data points are sampled with replacement. In contrast, sampling without replacement is far less understood, yet in practice it is very common, often easier to implement, and usually performs better. In this paper, we provide competitive convergence guarantees for without-replacement sampling under several scenarios, focusing on the natural regime of few passes over the data. Moreover, we describe a useful application of these results in the context of distributed optimization with randomly-partitioned data, yielding a nearly-optimal algorithm for regularized least squares (in terms of both communication complexity and runtime complexity) under broad parameter regimes. Our proof techniques combine ideas from stochastic optimization, adversarial online learning and transductive learning theory, and can potentially be applied to other stochastic optimization and learning problems.

Keywords: Learning Theory · Online Learning · Convex Optimization · Stochastic Methods
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Author Information

Ohad Shamir (Weizmann Institute of Science)

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