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arXiv · 1704.06731

Batch-Expansion Training: An Efficient Optimization Framework

Michał Dereziński·Dhruv Mahajan·S. Sathiya Keerthi·S. V. N. Vishwanathan·Markus Weimer
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Abstract

We propose Batch-Expansion Training (BET), a framework for running a batch optimizer on a gradually expanding dataset. As opposed to stochastic approaches, batches do not need to be resampled i.i.d. at every iteration, thus making BET more resource efficient in a distributed setting, and when disk-access is constrained. Moreover, BET can be easily paired with most batch optimizers, does not require any parameter-tuning, and compares favorably to existing stochastic and batch methods. We show that when the batch size grows exponentially with the number of outer iterations, BET achieves optimal $O(1/\epsilon)$ data-access convergence rate for strongly convex objectives. Experiments in parallel and distributed settings show that BET performs better than standard batch and stochastic approaches.

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Michał Dereziński, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer. 2017-04-22. Batch-Expansion Training: An Efficient Optimization Framework. https://arxiv.org/abs/1704.06731

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