25 citations · 29 across the 4 of their papers we have counts for
5 papers
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…
MarioGPT: Open-Ended Text2Level Generation through Large Language Models
Shyam Sudhakaran, Miguel González-Duque, Claire Glanois +3
Procedural Content Generation (PCG) is a technique to generate complex and diverse environments in an automated way. However, while generating content with PCG methods is often str…
Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry
Miguel González-Duque, Rasmus Berg Palm, Søren Hauberg +1
Deep generative models can automatically create content of diverse types. However, there are no guarantees that such content will satisfy the criteria necessary to present it to en…
Pulling back information geometry
Georgios Arvanitidis, Miguel González-Duque, Alison Pouplin +2
Latent space geometry has shown itself to provide a rich and rigorous framework for interacting with the latent variables of deep generative models. The existing theory, however, r…