We present the Variational Adaptive Newton (VAN) method which is a black-box optimization method especially suitable for explorative-learning tasks such as active learning and reinforcement learning. Similar to Bayesian methods, VAN estimates a distribution that can be used for exploration, but requires computations that are similar to continuous optimization methods. Our theoretical contribution reveals that VAN is a second-order method that unifies existing methods in distinct fields of continuous optimization, variational inference, and evolution strategies. Our experimental results show that VAN performs well on a wide-variety of learning tasks. This work presents a general-purpose explorative-learning method that has the potential to improve learning in areas such as active learning and reinforcement learning.
Cite
@article{arxiv.1711.05560,
title = {Variational Adaptive-Newton Method for Explorative Learning},
author = {Mohammad Emtiyaz Khan and Wu Lin and Voot Tangkaratt and Zuozhu Liu and Didrik Nielsen},
journal= {arXiv preprint arXiv:1711.05560},
year = {2017}
}