Unifying Likelihood-free Inference with Black-box Optimization and Beyond

Dinghuai Zhang; Jie Fu; Yoshua Bengio; Aaron Courville

ICLR 2022spotlight28 citations

Unifying Likelihood-free Inference with Black-box Optimization and Beyond

Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron Courville

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.

biological sequence designblack-box optimizationlikelihood-free inferenceBayesian inference

BibTeX

@inproceedings{
zhang2022unifying,
title={Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond},
author={Dinghuai Zhang and Jie Fu and Yoshua Bengio and Aaron Courville},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=1HxTO6CTkz}
}