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Poster
Scalable Adaptive Stochastic Optimization Using Random Projections
Gabriel Krummenacher · Brian McWilliams · Yannic Kilcher · Joachim M Buhmann · Nicolai Meinshausen

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #19 #None
in Wednesday Posters »

Adaptive stochastic gradient methods such as AdaGrad have gained popularity in particular for training deep neural networks. The most commonly used and studied variant maintains a diagonal matrix approximation to second order information by accumulating past gradients which are used to tune the step size adaptively. In certain situations the full-matrix variant of AdaGrad is expected to attain better performance, however in high dimensions it is computationally impractical. We present Ada-LR and RadaGrad two computationally efficient approximations to full-matrix AdaGrad based on randomized dimensionality reduction. They are able to capture dependencies between features and achieve similar performance to full-matrix AdaGrad but at a much smaller computational cost. We show that the regret of Ada-LR is close to the regret of full-matrix AdaGrad which can have an up-to exponentially smaller dependence on the dimension than the diagonal variant. Empirically, we show that Ada-LR and RadaGrad perform similarly to full-matrix AdaGrad. On the task of training convolutional neural networks as well as recurrent neural networks, RadaGrad achieves faster convergence than diagonal AdaGrad.

Keywords: Deep Learning or Neural Networks · Convex Optimization · (Other) Optimization
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Author Information

Gabriel Krummenacher (ETH Zurich)
Brian McWilliams (Disney Research)
Yannic Kilcher (ETH Zurich)
Joachim M Buhmann (ETH Zurich)
Nicolai Meinshausen (ETH Zurich)

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