188 citations · 317 across the 11 of their papers we have counts for
3 papers · 1 filter
Kermut: Composite kernel regression for protein variant effects
Peter Mørch Groth, Mads Herbert Kerrn, Lars Olsen +2
Reliable prediction of protein variant effects is crucial for both protein optimization and for advancing biological understanding. For practical use in protein engineering, it is…
A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences
Miguel González-Duque, Richard Michael, Simon Bartels +3
Optimizing discrete black-box functions is key in several domains, e.g. protein engineering and drug design. Due to the lack of gradient information and the need for sample efficie…
A Continuous Relaxation for Discrete Bayesian Optimization
Richard Michael, Simon Bartels, Miguel González-Duque +4
To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization. We propose a continuous relaxation of the object…