Since we posted our paper on “Learning to Optimize” last year, the area of optimizer learning has received growing attention. In this article, we provide an introduction to this line of work and share our perspective on the opportunities and challenges in this area.
Machine learning has enjoyed tremendous success and is being applied to a wide variety of areas, both in AI and beyond. This success can be attributed to the data-driven philosophy that underpins machine learning, which favours automatic discovery of patterns from data over manual design of systems using expert knowledge.
Yet, there is a paradox in the current paradigm: the algorithms that power machine learning are still designed manually. This raises a natural question: can we learn these algorithms instead? This could open up exciting possibilities: we could find new algorithms that perform better than manually designed algorithms, which could in turn improve learning capability.
The learned optimizer could potentially pick better update steps than traditional optimizers.
Doing so, however, requires overcoming a fundamental obstacle: how do we parameterize the space of algorithms so that it is both (1) expressive, and (2) efficiently searchable? Various ways of representing algorithms trade off these two goals. For example, if the space of algorithms is represented by a small set of known algorithms, it most likely does not contain the best possible algorithm, but does allow for efficient searching via simple enumeration of algorithms in the set. On the other hand, if the space of algorithms is represented by the set of all possible programs, it contains the best possible algorithm, but does not allow for efficient searching, as enumeration would take exponential time.
One of the most common types of algorithms used in machine learning is continuous optimization algorithms. Several popular algorithms exist, including gradient descent, momentum, AdaGrad and ADAM. We consider the problem of automatically designing such algorithms. Why do we want to do this? There are two reasons: first, many optimization algorithms are devised under the assumption of convexity and applied to non-convex objective functions; by learning the optimization algorithm under the same setting as it will actually be used in practice, the learned optimization algorithm could hopefully achieve better performance. Second, devising new optimization algorithms manually is usually laborious and can take months or years; learning the optimization algorithm could reduce the amount of manual labour.