Missing data pose significant challenges during representation learning of observational
data. The incompleteness of data can result in a deterioration of generative performance in …

Learning disentangled representations is important in representation learning, aiming to
learn a low dimensional representation of data where each dimension corresponds to one …

The advancements in disentangled representation learning significantly enhance the
accuracy of counterfactual predictions by granting precise control over instrumental …

Existing causal representation learning methods are based on the causal graph they build.
However, due to the omission of bias within the causal graph, they essentially encourage …

Learning disentangled causal representations is a challenging problem that has gained
significant attention recently due to its implications for extracting meaningful information for …

Generative modeling and self-supervised learning have in recent years made great strides
towards learning from data in a completely unsupervised way. There is still however an …

Targeting to understand the underlying explainable factors behind observations and
modeling the conditional generation process on these factors, we connect disentangled …

Abstract Representation learners that disentangle factors of variation have already proven to
be important in addressing various real world concerns such as fairness and interpretability …

Abstract Representation learning on the attribute-missing graphs, whose connection
information is complete while the attribute information of some nodes is missing, is an …

Modern scientific research and applications very often encounter 'fragmentary data'which
brings big challenges to imputation and prediction. By leveraging the structure of response …