Conformal Prediction Under Feedback Covariate Shift for Biomolecular Design

Understand

Many applications of machine learning methods involve an iterative protocol in which data are collected, a model is trained, and then outputs of that model are used to choose what data to consider next.

• For example, one data-driven approach for designing proteins is to train a regression model to predict the fitness of protein sequences, then use it to propose new sequences believed to exhibit greater fitness than observed in the training data.
• Since validating designed sequences in the wet lab is typically costly, it is important to quantify the uncertainty in the model's predictions.
• This is challenging because of a characteristic type of distribution shift between the training and test data in the design setting -- one in which the training and test data are statistically dependent, as the latter is chosen based on the former.