Squint provides a codebase to perform numerical proof-of-concept experiments in learning theory, particularly in Prediction with Expert Advice problems, a core problem in learning theory.
The software has been developed in the Machine Learning group and is a companion to the research paper 'Second-order Quantile Methods for Experts and Combinatorial Games'. The algorithm is provably robust, was designed to be highly adaptive, and performs excellently in a broad spectrum of practical environments.
The MetaGrad algorithm can be regarded as a successor to Squint, in the sense that it applies to a more general problem, i.e. Online Convex Optimization.
