Conformal Inference Optimization for Biopolymer Sequences
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Solution Overview
Problem
Current machine learning methods for Bayesian optimization are poorly suited for discrete, high-dimensional spaces like biopolymer sequences, as they rely on Gaussian process priors that fail to provide accurate function estimates and calibrated uncertainties, especially in low-data regimes, and approximate inference methods often result in poorly reflective posterior estimates.
Innovation Solution
The use of a neural network as a surrogate function, combined with conformal inference optimization, which calculates confidence intervals based on nearest neighbors to provide a more accurate and calibrated uncertainty estimation, allowing for optimized biopolymer sequence design by selecting candidate sequences with an optimized linear combination of conformal inference intervals and predicted values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Gaussian process priors are used to model the objective function in Bayesian optimization, then theoretical guarantees are established, but the method is poorly-suited for discrete, high-dimensional, combinatorial spaces and provides poor function estimates
Solution Approach 1:
The patent changes the fundamental parameters of the optimization approach by replacing Gaussian process priors with conformal inference based on nearest-neighbor search. This allows the method to work in discrete, high-dimensional combinatorial spaces while maintaining validity guarantees through the conformal prediction framework, thus resolving the contradiction between theoretical guarantees and suitability for discrete spaces
Solution Approach 2:
The patent substitutes the Gaussian process mechanical system with a conformal inference system based on distance metrics and nearest-neighbor search. This replacement maintains the Bayesian optimization framework while using fundamentally different mathematical mechanisms that are appropriate for discrete, high-dimensional spaces
2Measurement precision
If neural networks are used as surrogate functions, then more accurate function estimates and arbitrary model complexity are achieved, but fully-Bayesian treatment of uncertainty becomes intractable
Solution Approach 1:
The patent extracts the uncertainty quantification component from the neural network and handles it separately through conformal inference. The neural network provides accurate function estimates while the conformal prediction framework independently provides validity guarantees, avoiding the need for intractable fully-Bayesian treatment of neural network uncertainty
Solution Approach 2:
The patent segments the uncertainty estimation problem into two parts: the neural network handles function estimation while conformal inference handles uncertainty calibration. This segmentation allows each component to operate in its optimal regime without the computational burden of joint Bayesian treatment
3Productivity
If approximate inference methods are used for neural networks, then computational tractability is achieved, but the posterior estimates poorly reflect the true posterior
Solution Approach 1:
The patent introduces conformal inference as an intermediary layer between the neural network predictions and the final uncertainty estimates. This intermediary recalibrates the neural network outputs to provide valid posterior estimates that both reflect computational efficiency and maintain statistical reliability
Data Source
AI summary
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.