3 papers
cs.LG2024
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…
cs.RO2024
Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds
Noémie Jaquier, Leonel Rozo, Miguel González-Duque +2
Human motion taxonomies serve as high-level hierarchical abstractions that classify how humans move and interact with their environment. They have proven useful to analyse grasps,…
cs.LG2024
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…