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    Conformal Inference for Optimization
    1.
    发明申请
    Conformal Inference for Optimization 有权

    公开(公告)号：US20230122168A1

    公开(公告)日：2023-04-20

    申请号：US17759838

    申请日：2021-01-29

    申请人： Flagship Pioneering Innovations VI, LLC

    发明人： Molly Krisann Gibson ,  Kevin Kaichuang Yang ,  Maxim Baranov ,  Andrew Lane Beam

    IPC分类号： G16B30/00 ,  G16B40/00

    摘要： Accurate function estimations and well-calibrated uncertainties are important for Bayesian optimization (BO). Most theoretical guarantees for BO are established for methods that model the objective function with a surrogate drawn from a Gaussian process (GP) prior. GP priors are poorly-suited for discrete, high-dimensional, combinatorial spaces, such as biopolymer sequences. Using a neural network (NN) as the surrogate function can obtain more accurate function estimates. Using a NN can allow arbitrarily complex models, removing the GP prior assumption, and enable easy pretraining, which is beneficial in the low-data BO regime. However, a fully-Bayesian treatment of uncertainty in NNs remains intractable, and existing approximate methods, like Monte Carlo dropout and variational inference, can highly miscalibrate uncertainty estimates. Conformal Inference Optimization (CI-OPT) uses confidence intervals calculated using conformal inference as a replacement for posterior uncertainties in certain BO acquisition functions. A conformal scoring function with properties amenable for optimization is effective on standard BO datasets and real-world protein datasets.