Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

Abstract Diffusion Probabilistic Models (DPMs) have recently demonstrated impressive
results on various generative tasks. Despite its promises, the learned representations of pre …

Text-to-motion generation is a formidable task, aiming to produce human motions that align
with the input text while also adhering to human capabilities and physical laws. While there …

Abstract Machine learning models face generalization challenges when exposed to out-of-
distribution (OOD) samples with unforeseen distribution shifts. Recent research reveals that …

In this paper, we make a bold attempt toward an ambitious task: given a pre-trained
classifier, we aim to reconstruct an image generator, without relying on any data samples …

Conditional diffusion models serve as the foundation of modern image synthesis and find
extensive application in fields like computational biology and reinforcement learning. In …

Domain generalization aims to learn a generalization model that can perform well on
unseen test domains by only training on limited source domains. However, existing domain …

Recent developments in diffusion models have advanced conditioned image generation, yet
they struggle with reconstructing out-of-distribution (OOD) images, such as unseen tumors in …

Abstract Machine learning-based algorithms demonstrate impressive performance across
numerous fields; however, they continue to suffer from certain limitations. Even sophisticated …

Invariant representation learning (IRL) encourages the prediction from invariant causal
features to labels deconfounded from the environments, advancing the technical roadmap of …