Unifying Likelihood-Free Inference with Black-Box Optimization and Beyond
Abstract
Black-box optimization formulations for biological sequence design have drawn recent attention due to their promising potential impact on the pharmaceutical industry. In this work, we propose to unify two seemingly distinct worlds: likelihood-free inference and black-box optimization, under one probabilistic framework. In tandem, we provide a recipe for constructing various sequence design methods based on this framework. We show how previous optimization approaches can be "reinvented" in our framework, and further propose new probabilistic black-box optimization algorithms. Extensive experiments on sequence design application illustrate the benefits of the proposed methodology.
Cite
Text
Zhang et al. "Unifying Likelihood-Free Inference with Black-Box Optimization and Beyond." International Conference on Learning Representations, 2022.Markdown
[Zhang et al. "Unifying Likelihood-Free Inference with Black-Box Optimization and Beyond." International Conference on Learning Representations, 2022.](https://mlanthology.org/iclr/2022/zhang2022iclr-unifying/)BibTeX
@inproceedings{zhang2022iclr-unifying,
title = {{Unifying Likelihood-Free Inference with Black-Box Optimization and Beyond}},
author = {Zhang, Dinghuai and Fu, Jie and Bengio, Yoshua and Courville, Aaron},
booktitle = {International Conference on Learning Representations},
year = {2022},
url = {https://mlanthology.org/iclr/2022/zhang2022iclr-unifying/}
}