Learning to Learn without Gradient Descent by Gradient Descent
  We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfer in that they can be used to efficiently optimize a broad range of derivative-free black-box functions, including Gaussian process bandits, simple control objectives, global optimization benchmarks and hyper-parameter tuning tasks. Up to the training horizon, the learned optimizers learn to trade-off exploration and exploitation, and compare favourably with heavily engineered Bayesian optimization packages for hyper-parameter tuning. 
Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha   Denil, Timothy P. Lillicrap, Matt Botvinick, Nando de Freitas
stat.ML cs.LG