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Title: The Case for Full-Matrix Adaptive Regularization

Paper content:
Adaptive regularization methods pre-multiply a descent direction by a preconditioning matrix.
Due to the large number of parameters of machine learning problems, full-matrix preconditioning methods are prohibitively expensive.
We show how to modify full-matrix adaptive regularization in order to make it practical and effective.
We also provide novel theoretical analysis
for adaptive regularization in non-convex optimization settings.
The core of our algorithm, termed GGT, consists of efficient inverse computation of square roots of low-rank matrices.
Our preliminary experiments underscore improved convergence rate of GGT across a variety of synthetic tasks and standard deep learning benchmarks.
Stochastic gradient descent is the workhorse behind the recent deep learning revolution.
This simple and age-old algorithm has been supplemented with a variety of enhancements to improve its practical performance, and sometimes its theoretical guarantees.Amongst the acceleration methods there are three main categories: momentum, adaptive regularization, and variance reduction.
Momentum (in its various incarnations, like heavy-ball or Nesterov acceleration) is the oldest enhancement.
It has a well-developed theory, and is known to improve practical convergence in a variety of tasks, small and large.
It is also easy to implement.
Variance reduction is the most recent advancement; in theory and practice, it is mostly applicable to convex optimization, and is thus less influential in deep learning.This brings us to adaptive regularization: the most sophisticated, hard to implement, and debated acceleration method.
While state-of-the-art optimizers such as Adam and AdaGrad (Kingma & Ba, 2014; BID13 do use adaptive regularization, they do so in a very limited form: with diagonal matrices, often marketed as per-coordinate adaptive learning-rate methods.
Despite solid theoretical guarantees, the practical value of diagonal adaptive regularization as compared to "vanilla" SGD has been the subject of much debate BID48 .
However, the efficacy of full-matrix adaptive regularization has been relatively unexplored.
This is due to the prohibitive computational cost associated with full-matrix operations: full AdaGrad requires taking the inverse square root of a large matrix.In this paper, we present GGT, a practical solution to the computational problems plaguing fullmatrix adaptive regularization, making this technique scalable for modern deep models.
At the heart of our method is a simple, GPU-friendly way to apply the inverse square root of the low-rank second-moment matrix of recent gradients; see FIG0 .
GGT's running time is comparable to state-of-the-art optimizers.We proceed to show that full-matrix preconditioning allows for much better exploitation of anisotropic curvature in loss landscapes.
First, we show synthetic experiments which demonstate clear benefits of GGT over baselines, especially when the problem is ill-conditioned.
Then, we implement GGT at scale, and show that the benefits translate to faster training on standard deep learning benchmarks.
Our improvement is most salient in complicated landscapes like RNN training.Our algorithm comes with theoretical guarantees.
We give the first proof of convergence to firstorder critical points for an algorithm with adaptive regularization in a stochastic non-convex setting, featuring a rate which is dependent on an adaptive ratio.
We show examples where our bound is stronger than that for SGD, providing some theoretical basis for our empirical findings.
This work investigates full-matrix adaptive regularization: our main contribution is to make this technique viable for large-scale optimization, by a method for efficient multiplication by the inverse square root of a full second-moment matrix over a short window of gradients.
This leads to a new algorithm, GGT, a truly scalable optimization algorithm with full-matrix adaptive preconditioning.Through synthetic experiments, we have shown that GGT accelerates optimization in ill-conditioned loss landscapes; this is supported by accompanying adaptive convergence guarantees.
Preliminary experiments show accelerated convergence on standard deep learning benchmarks, with very different training dynamics from existing diagonal adaptive methods.
We accompany our algorithm and experiments with the first theoretical characterization of the benefits of adaptive regularization in a non-convex setting.
We hope that GGT will be the first of a new class of algorithms for the modern large-scale optimization toolbox, and to foster new discussion towards an ever-elusive understanding of loss landscapes in deep learning.

assistant: text: fast, truly scalable full-matrix AdaGrad/Adam, with theory for adaptive stochastic non-convex optimization