Diffusion models, a powerful and universal generative artificial intelligence technology, have
achieved tremendous success and opened up new possibilities in diverse applications. In …

Generative flow networks (GFlowNets) are a family of algorithms for training a sequential
sampler of discrete objects under an unnormalized target density and have been …

The goal of offline black-box optimization (BBO) is to optimize an expensive black-box
function using a fixed dataset of function evaluations. Prior works consider forward …

Diffusion models, a powerful and universal generative AI technology, have achieved
tremendous success in computer vision, audio, reinforcement learning, and computational …

In offline model-based optimization, we strive to maximize a black-box objective function by
only leveraging a static dataset of designs and their scores. This problem setting arises in …

Experimental design is a fundamental problem in many science and engineering fields. In
this problem, sample efficiency is crucial due to the time, money, and safety costs of real …

In this work, we decouple the iterative bi-level offline RL (value estimation and policy
extraction) from the offline training phase, forming a non-iterative bi-level paradigm and …

Offline model-based optimization aims to find a design that maximizes a property of interest
using only an offline dataset, with applications in robot, protein, and molecule design …

We study offline model-based optimization to maximize a black-box objective function with a
static dataset of designs and scores. These designs encompass a variety of domains …

Bi-level optimization, especially the gradient-based category, has been widely used in the
deep learning community including hyperparameter optimization and meta-knowledge …