Improving the convergence of SGD through adaptive batch sizes

Authors: Scott Sievert, Shrey Shah
Primary area: optimization
Keywords: stochastic gradient descent, batch size

Abstract:
Mini-batch stochastic gradient descent (SGD) and variants thereof
    approximate the objective function's gradient with a small number of
    training examples, aka the batch size.  Small batch sizes require little
    computation for each model update but can yield high-variance gradient
    estimates, which poses some challenges for optimization. Conversely, large
    batches require more computation but can yield higher precision gradient
    estimates. This work presents a method to adapt the batch size to the
    model's training loss. For various function classes, we show that our
    method requires the same order of model updates as gradient descent while
    requiring the same order of gradient computations as SGD. This method
    requires evaluating the model's loss on the entire dataset every model
    update. However, the required computation is greatly reduced by
    approximating the training loss. We provide experiments that illustrate our
    methods require fewer model updates without increasing the total amount of
    computation.

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