The Marginal Value of Adaptive Gradient Methods in Machine Learning The Marginal Value of Adaptive Gradient Methods in Machine Learning
Paper summary In modern machine learning, gradient descent has diversified into a zoo of subtly distinct techniques, all designed, analytically, heuristically, or practically, to ease or accelerate our model’s path through multidimensional loss space. A solid contingent of these methods are Adaptive Gradient methods, which scale the size of gradient updates according to variously calculated historical averages or variances of the vector update, which has the effect of scaling down the updates along feature dimensions that have experienced large updates in the past. The intuition behind this is that we may want to effectively reduce the learning rate (by dividing by a larger number) along dimensions where there have been large or highly variable updates. These methods are commonly used because, as the name suggests, they update to the scale of your dataset and particular loss landscape, avoiding what might otherwise be a lengthy process of hyperparameter tuning. But this paper argues that, at least on a simplified problem, adaptive methods can reach overly simplified and overfit solutions that generalize to test data less well than a non-adaptive, more standard gradient descent method. The theoretical core of the paper is a proof showing limitations of the solution reached by adaptive gradient on a simple toy regression problem, on linearly separable data. It’s a little dodgy to try to recapitulate a mathematical proof in verbal form, but I’ll do my best, on the understanding that you should really read the fully thing to fully verify the logic. The goal of the proof is to characterize the solution weight vector learned by different optimization systems. In this simplified environment, a core informational unit of your equations is X^T(y), which (in a world where labels are either -1 or 1), goes through each feature, and for each feature, takes a dot product between that feature vector (across examples) and the label vector, which has the effect of adding up a positive sum of all the feature values attached to positive examples, and then subtracting out (because of the multiply by -1) all the feature values attached to positive examples. When this is summed, we get a per feature value that will be positive if positive values of the feature tend to indicate positive labels, and negative if the opposite is true, in each case with a magnitude relating to the strength of that relationship. The claim made by the paper, supported by a lot of variable transformations, is that the solution learned by Adaptive Gradient methods reduces to a sign() operation on top of that vector, where magnitude information is lost. This happens because the running gradients that you divide out happen to correspond to the absolute value of this vector, and dividing a vector (which would be the core of the solution in the non-adaptive case) by its absolute value gives you a simple sign. The paper then goes on to show that this edge case can lead to cases of pathological overfitting in cases of high feature dimensionality relative to data points. (I wish I could give more deep insight on why this is the case, but I wasn’t really able to translate the math into intuition, outside of this fact of scaling by gradient magnitudes having the effect of losing potentially useful gradient information. The big question from all this is...does this matter? Does it matter, in particular, beyond a toy dataset, and an artificially simple problem? The answer seems to be a pretty strong maybe. The authors test adaptive methods against hyperparameter-optimized SGD and momentum SGD (a variant, but without the adaptive aspects), and find that, while adaptive methods often learn more quickly at first, SGD approaches pick up later in training, first in terms of test set error at a time when adaptive methods’ training set error still seems to be decreasing, and later even in training set error. So there seems to be evidence that solutions learned by adaptive methods generalize worse than ones learned by SGD, at least on some image recognition and language-RNN models. (Though, interestingly, RMS-Prop comes close to the SGD test set levels, doing the best out of the adaptive methods). Overall, this suggests to me that doing fully hyperparameter optimized SGD might be a stronger design choice, but that adaptive methods retain popularity because of their (very appealing, practically) lack of need for hyperparameter tuning to at least to a *reasonable* job, even if their performance might have more of a ceiling than that of vanilla SGD.
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The Marginal Value of Adaptive Gradient Methods in Machine Learning
Wilson, Ashia C. and Roelofs, Rebecca and Stern, Mitchell and Srebro, Nathan and Recht, Benjamin
arXiv e-Print archive - 2017 via Local Bibsonomy
Keywords: dblp


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