Design by adaptive sampling
This work addresses input design problems, such as optimizing DNA sequences or images, for researchers and practitioners in fields like bioinformatics and computer vision, though it appears incremental as it builds on existing methods.
The authors tackled the problem of input design by combining unsupervised generative models with black box predictive models to find inputs that maximize or achieve specified properties, demonstrating that their approach substantially outperforms other recent methods in deterministic and unbiased cases.
We present a probabilistic modeling framework and adaptive sampling algorithm wherein unsupervised generative models are combined with black box predictive models to tackle the problem of input design. In input design, one is given one or more stochastic "oracle" predictive functions, each of which maps from the input design space (e.g. DNA sequences or images) to a distribution over a property of interest (e.g. protein fluorescence or image content). Given such stochastic oracles, the problem is to find an input that is expected to maximize one or more properties, or to achieve a specified value of one or more properties, or any combination thereof. We demonstrate experimentally that our approach substantially outperforms other recently presented methods for tackling a specific version of this problem, namely, maximization when the oracle is assumed to be deterministic and unbiased. We also demonstrate that our method can tackle more general versions of the problem.