AI-driven design problems, such as DNA/protein sequence design, are commonly tackled
from two angles: generative modeling, which efficiently captures the feasible design space …

This paper aims to develop and provide a rigorous treatment to the problem of entropy
regularized fine-tuning in the context of continuous-time diffusion models, which was …

In recent years, the exceptional performance of generative models in generative tasks has
sparked significant interest in their integration into decision-making processes. Due to their …

The Cambrian explosion of easily accessible pre-trained diffusion models suggests a
demand for methods that combine multiple different pre-trained diffusion models without …

Optimizing complex and high-dimensional black-box functions is ubiquitous in science and
engineering fields. Unfortunately, the online evaluation of these functions is restricted due to …

While one commonly trains large diffusion models by collecting datasets on target
downstream tasks, it is often desired to align and finetune pretrained diffusion models on …

Optimizing high-dimensional and complex black-box functions is crucial in numerous
scientific applications. While Bayesian optimization (BO) is a powerful method for sample …

Black-box optimization (BBO) aims to optimize an objective function by iteratively querying a
black-box oracle in a sample-efficient way. While prior studies focus on forward approaches …

While one commonly trains large diffusion models by collecting datasets on target
downstream tasks, it is often desired to finetune pretrained diffusion models on some reward …

The Cambrian explosion of easily accessible pre-trained diffusion models suggests a
demand for methods that combine multiple different pre-trained diffusion models without …