PuSH - Publication Server of Helmholtz Zentrum München

Eismann, S.* ; Levy, D.* ; Shu, R.* ; Bartzsch, S.

Bayesian optimization and attribute adjustment.

In: (34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018, 6-10 August 2018, Monterey, United States). 2018. 1042-1052 ( ; 2)
Automatic design via Bayesian optimization holds great promise given the constant increase of available data across domains. However, it faces difficulties from high-dimensional, potentially discrete, search spaces. We propose to probabilistically embed inputs into a lower dimensional, continuous latent space, where we perform gradient-based optimization guided by a Gaussian process. Building on variational autoncoders, we use both labeled and unlabeled data to guide the encoding and increase its accuracy. In addition, we propose an adversarial extension to render the latent representation invariant with respect to specific design attributes, which allows us to transfer these attributes across structures. We apply the framework both to a functional-protein dataset and to perform optimization of drag coefficients directly over high-dimensional shapes without incorporating domain knowledge or handcrafted features.
Additional Metrics?
[➜Log in]
Edit extra informations Login
Publication type Article: Conference contribution
Corresponding Author
ISSN (print) / ISBN 9781510871601
Conference Title 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
Conference Date 6-10 August 2018
Conference Location Monterey, United States
Quellenangaben Volume: 2, Issue: , Pages: 1042-1052 Article Number: , Supplement: ,
Non-patent literature Publications
Institute(s) Institute of Radiation Medicine (IRM)