Unifying Likelihood-free Inference with Black-box Optimization and BeyondDownload PDF

29 Sept 2021, 00:31 (modified: 06 Feb 2022, 05:07)ICLR 2022 SpotlightReaders: Everyone
Keywords: biological sequence design, black-box optimization, likelihood-free inference, Bayesian inference
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.
One-sentence Summary: We propose a framework to unify likelihood-free inference and black-box sequence design and further propose novel sequence design algorithms based on the framework.
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