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Black-box optimization formulations for biological sequence design have drawnrecent attention due to their promising potential impact on the pharmaceuticalindustry. In this work, we propose to unify two seemingly distinct worlds:likelihood-free inference and black-box optimization, under one probabilisticframework. In tandem, we provide a recipe for constructing various sequencedesign methods based on this framework. We show how previous optimizationapproaches can be "reinvented" in our framework, and further propose newprobabilistic black-box optimization algorithms. Extensive experiments onsequence design application illustrate the benefits of the proposedmethodology.

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