143 citations · 147 across the 2 of their papers we have counts for
3 papers
cs.LG2019★ 143 cited
Scalable Global Optimization via Local Bayesian Optimization
David Eriksson, Michael Pearce, Jacob R Gardner +2
Bayesian optimization has recently emerged as a popular method for the sample-efficient optimization of expensive black-box functions. However, the application to high-dimensional…
stat.ML2018
Metropolis-Hastings Generative Adversarial Networks
Ryan Turner, Jane Hung, Eric Frank +2
We introduce the Metropolis-Hastings generative adversarial network (MH-GAN), which combines aspects of Markov chain Monte Carlo and GANs. The MH-GAN draws samples from the distrib…
stat.ML2017★ 4 cited
How well does your sampler really work?
Ryan Turner, Brady Neal
We present a new data-driven benchmark system to evaluate the performance of new MCMC samplers. Taking inspiration from the COCO benchmark in optimization, we view this task as hav…