Deep generative models (DGMs) have demonstrated great success across various domains,
particularly in generating texts, images, and videos using models trained from offline data …

While machine learning models are typically trained to solve prediction problems, we might
often want to use them for optimization problems. For example, given a dataset of proteins …

Offline Black-Box Optimization (BBO) aims at optimizing a black-box function using the
knowledge from a pre-collected offline dataset of function values and corresponding input …

In computational chemistry, crystal structure prediction (CSP) is an optimization problem that
involves discovering the lowest energy stable crystal structure for a given chemical formula …

Black-box optimization algorithms have been widely used in various machine learning
problems, including reinforcement learning and prompt fine-tuning. However, directly …

Offline model-based optimization (MBO) aims to identify a design that maximizes a black-
box function using only a fixed, pre-collected dataset of designs and their corresponding …

Despite notable successes of Reinforcement Learning (RL), the prevalent use of an online
learning paradigm prevents its widespread adoption, especially in hazardous or costly …

In computational chemistry, crystal structure prediction (CSP) is an optimization problem that
involves discovering the lowest energy stable crystal structure for a given chemical formula …

Offline reinforcement learning algorithms often require careful hyperparameter tuning.
Consequently, before deployment, we need to select amongst a set of candidate policies. As …

Data-driven black-box model-based optimization (MBO) problems arise in a great number of
practical application scenarios, where the goal is to find a design over the whole space …