Recent success of deep learning is largely attributed to the sheer amount of data used for
training deep neural networks. Despite the unprecedented success, the massive data …

In this paper, we study dataset distillation (DD), from a novel perspective and introduce
a\emph {dataset factorization} approach, termed\emph {HaBa}, which is a plug-and-play …

Dataset distillation, also known as dataset condensation, aims to compress a large dataset
into a compact synthetic one. Existing methods perform dataset condensation by assuming a …

Dataset Condensation is a newly emerging technique aiming at learning a tiny dataset that
captures the rich information encoded in the original dataset. As the size of datasets …

In this paper, we consider a real-world scenario where a model that is trained on pre-defined
classes continually encounters unlabeled data that contains both known and novel classes …

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

In this paper, we tackle the problem of domain shift. Most existing methods perform training
on multiple source domains using a single model, and the same trained model is used on all …

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 …

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 …