Motivation Protein representation learning methods have shown great potential to many
downstream tasks in biological applications. A few recent studies have demonstrated that …

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 …

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 …

We study the problem of optimizing biological sequences, eg, proteins, DNA, and RNA, to
maximize a black-box score function that is only evaluated in an offline dataset. We propose …

Protein representation learning has primarily benefited from the remarkable development of
language models (LMs). Accordingly, pre-trained protein models also suffer from a problem …

Herein, we propose a novel dataset distillation method for constructing small informative
datasets that preserve the information of the large original datasets. The development of …

Addressing real-world optimization problems becomes particularly challenging when
analytic objective functions or constraints are unavailable. While numerous studies have …

Protein representation learning plays a crucial role in understanding the structure and
function of proteins, which are essential biomolecules involved in various biological …