# Tomer Koren

I'm a Senior Research Scientist at Google Research, Tel Aviv, and an Assistant Professor at the Blavatnik School of Computer Science at Tel Aviv University. My research interests are in machine learning and optimization.

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Stochastic Optimization with Laggard Data Pipelines

Cyril Zhang

Kunal Talwar

Rohan Anil

Thirty-fourth Conference on Neural Information Processing Systems, 2020 (2020) (to appear)

Preview abstract
State-of-the-art optimization has increasingly moved toward massively parallel pipelines with extremely large batches. As a consequence, the performance bottleneck is shifting towards the CPU- and disk-bound data loading and preprocessing, as opposed to hardware-accelerated backpropagation. In this regime, a recently proposed approach is data echoing (Choi et al. '19), which takes repeated gradient steps on the same batch. We provide the first convergence analysis of data echoing-based extensions of ubiquitous optimization methods, exhibiting provable improvements over their synchronous counterparts. Specifically, we show that asynchronous batch reuse can magnify the gradient signal in a stochastic batch, without harming the statistical rate.
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