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Found 9 papers, 3 papers with code
Is novelty predictable?
When designing objects to achieve novel property values with machine learning, one faces a fundamental challenge: how to push past the frontier of current knowledge, distilled from the training data into the model, in a manner that rationally controls the risk of failure.
Machine Learning for Protein Engineering
Directed evolution of proteins has been the most effective method for protein engineering.
Prediction-Powered Inference
Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system.
Conformal prediction for the design problem
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.
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence
We thus propose a novel combination of optimization and sampling techniques for approximate Bayesian inference by constructing an IS proposal distribution through the minimization of a forward KL (FKL) divergence.
Variational Refinement for Importance SamplingUsing the Forward Kullback-Leibler Divergence
Variational Inference (VI) is a popular alternative to asymptotically exact sampling in Bayesian inference.
Autofocused oracles for model-based design
The design goal is to construct an object with desired properties, such as a protein that binds to a therapeutic target, or a superconducting material with a higher critical temperature than previously observed.
A view of Estimation of Distribution Algorithms through the lens of Expectation-Maximization
We show that a large class of Estimation of Distribution Algorithms, including, but not limited to, Covariance Matrix Adaption, can be written as a Monte Carlo Expectation-Maximization algorithm, and as exact EM in the limit of infinite samples.
Hallucinations in Neural Machine Translation
Neural machine translation (NMT) systems have reached state of the art performance in translating text and are in wide deployment.
