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Found 3 papers, 1 papers with code
A Continuous Relaxation for Discrete Bayesian Optimization
To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization.
Probabilistic thermal stability prediction through sparsity promoting transformer representation
Pre-trained protein language models have demonstrated significant applicability in different protein engineering task.
Robust uncertainty estimates with out-of-distribution pseudo-inputs training
As one cannot train without data, we provide mechanisms for generating pseudo-inputs in informative low-density regions of the input space, and show how to leverage these in a practical Bayesian framework that casts a prior distribution over the model uncertainty.
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