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Found 5 papers, 3 papers with code
Joint Composite Latent Space Bayesian Optimization
To effectively tackle these challenges, we introduce Joint Composite Latent Space Bayesian Optimization (JoCo), a novel framework that jointly trains neural network encoders and probabilistic models to adaptively compress high-dimensional input and output spaces into manageable latent representations.
Inverse Protein Folding Using Deep Bayesian Optimization
Furthermore, it is challenging to adapt pure generative approaches to other settings, e. g., when constraints exist.
Black Box Adversarial Prompting for Foundation Models
Prompting interfaces allow users to quickly adjust the output of generative models in both vision and language.
Discovering Many Diverse Solutions with Bayesian Optimization
Bayesian optimization (BO) is a popular approach for sample-efficient optimization of black-box objective functions.
Local Latent Space Bayesian Optimization over Structured Inputs
By reformulating the encoder to function as both an encoder for the DAE globally and as a deep kernel for the surrogate model within a trust region, we better align the notion of local optimization in the latent space with local optimization in the input space.
