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Found 8 papers, 4 papers with code
Large Scale Multi-Task Bayesian Optimization with Large Language Models
As this feedback loop continues, we find that the LLM is eventually able to generate solutions to new tasks in just a few shots that are better than the solutions produced by "from scratch" by Bayesian optimization while simultaneously requiring significantly fewer oracle calls.
Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization
In multi-objective black-box optimization, the goal is typically to find solutions that optimize a set of $T$ black-box objective functions, $f_1$, ..., $f_T$, simultaneously.
Approximation-Aware Bayesian Optimization
Our approach outperforms standard SVGPs on high-dimensional benchmark tasks in control and molecular design.
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.
