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Found 4 papers, 3 papers with code
Generative Adversarial Bayesian Optimization for Surrogate Objectives
To address this limitation, we propose generative adversarial Bayesian optimization (GABO) using adaptive source critic regularization, a task-agnostic framework for Bayesian optimization that employs a Lipschitz-bounded source critic model to constrain the optimization trajectory to regions where the surrogate function is reliable.
Inverse Protein Folding Using Deep Bayesian Optimization
Furthermore, it is challenging to adapt pure generative approaches to other settings, e. g., when constraints exist.
Learning Performance-Improving Code Edits
Next, we propose a broad range of adaptation strategies for code optimization; for prompting, these include retrieval-based few-shot prompting and chain-of-thought, and for finetuning, these include performance-conditioned generation and synthetic data augmentation based on self-play.
Cyclical Kernel Adaptive Metropolis
We propose cKAM, cyclical Kernel Adaptive Metropolis, which incorporates a cyclical stepsize scheme to allow control for exploration and sampling.
