Learned Optimizers: Learning to Learn Optimization Algorithms


Much as replacing hand-designed features with learned functions has revolutionized how we solve perceptual tasks, learned algorithms also have the potential to transform how we train machine learning models. Learned optimizers are one such learned algorithm. Instead of writing down mathematical expressions to perform optimization, a learned optimizer learns the function to perform optimization. This talk will outline how these learned optimizers work, and discuss a number of difficulties that arise when training them. Finally I will share some interesting behaviors which are starting to emerge.


Luke Metz is a research scientist at Google Brain working on meta-learning and learned optimizers. He's interested in building general purpose, learned learning algorithms that not only perform well, but generalizes to new types of never before seen problems.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google