Autism spectrum disorder (ASD) is highly heterogeneous. Identifying systematic individual
differences in neuroanatomy could inform diagnosis and personalized interventions. The …

Abstract Independent Component Analysis (ICA) aims to recover independent latent
variables from observed mixtures thereof. Causal Representation Learning (CRL) aims …

Masked autoencoder (MAE), a simple and effective self-supervised learning framework
based on the reconstruction of masked image regions, has recently achieved prominent …

Unsupervised multiplex graph representation learning (UMGRL) has received increasing
interest, but few works simultaneously focused on the common and private information …

We present a unified framework for studying the identifiability of representations learned
from simultaneously observed views, such as different data modalities. We allow a partially …

Contrastive learning is a cornerstone underlying recent progress in multi-view and
multimodal learning, eg, in representation learning with image/caption pairs. While its …

Identifying latent variables and causal structures from observational data is essential to
many real-world applications involving biological data, medical data, and unstructured data …

Recently, various methods have been introduced to address the OOD detection problem
with training outlier exposure. These methods usually count on discriminative softmax metric …

Self-supervised representation learning often uses data augmentations to induce some
invariance to" style" attributes of the data. However, with downstream tasks generally …

In recent years, deep learning approaches have gained significant attention in predicting
brain disorders using neuroimaging data. However, conventional methods often rely on …