no code implementations • 1 Jan 2021 • Daniel Pace, Alessandra Russo, Murray Shanahan
Inspired by Quality-Diversity algorithms, in this work we train a collection of classifiers to learn distinct solutions to a classification problem, with the goal of learning to exploit a variety of predictive signals present in the training data.
no code implementations • 12 Jun 2020 • Daniel Pace, Alessandra Russo, Murray Shanahan
assumption is a useful idealization that underpins many successful approaches to supervised machine learning.
