Training Deep Networks without Learning Rates Through Coin Betting
This addresses the bottleneck of learning rate tuning for deep learning practitioners, though it appears incremental as it builds on existing optimization methods.
The paper tackles the problem of hyperparameter tuning in deep learning by proposing a stochastic gradient descent procedure that eliminates the need for learning rate settings, achieving competitive performance with popular algorithms.
Deep learning methods achieve state-of-the-art performance in many application scenarios. Yet, these methods require a significant amount of hyperparameters tuning in order to achieve the best results. In particular, tuning the learning rates in the stochastic optimization process is still one of the main bottlenecks. In this paper, we propose a new stochastic gradient descent procedure for deep networks that does not require any learning rate setting. Contrary to previous methods, we do not adapt the learning rates nor we make use of the assumed curvature of the objective function. Instead, we reduce the optimization process to a game of betting on a coin and propose a learning-rate-free optimal algorithm for this scenario. Theoretical convergence is proven for convex and quasi-convex functions and empirical evidence shows the advantage of our algorithm over popular stochastic gradient algorithms.