Large-Scale Learning with Less RAM via Randomization


Daniel Golovin, D. Sculley, Brendan McMahan, Michael Young ;
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(2):325-333, 2013.


We reduce the memory footprint of popular large-scale online learning methods by projecting our weight vector onto a coarse discrete set using randomized rounding. Compared to standard 32-bit float encodings, this reduces RAM usage by more than 50% during training and by up 95% when making predictions from a fixed model, with almost no loss in accuracy. We also show that randomized counting can be used to implement per-coordinate learning rates, improving model quality with little additional RAM. We prove these memory-saving methods achieve regret guarantees similar to their exact variants. Empirical evaluation confirms excellent performance, dominating standard approaches across memory versus accuracy tradeoffs.

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