5 citations · 9 across the 8 of their papers we have counts for
8 papers
Causal Inference from Slowly Varying Nonstationary Processes
Kang Du, Yu Xiang
Causal inference from observational data following the restricted structural causal models (SCM) framework hinges largely on the asymmetry between cause and effect from the data ge…
Mining Invariance from Nonlinear Multi-Environment Data: Binary Classification
Austin Goddard, Kang Du, Yu Xiang
Making predictions in an unseen environment given data from multiple training environments is a challenging task. We approach this problem from an invariance perspective, focusing…
Low-Rank Approximation of Structural Redundancy for Self-Supervised Learning
Kang Du, Yu Xiang
We study the data-generating mechanism for reconstructive SSL to shed light on its effectiveness. With an infinite amount of labeled samples, we provide a sufficient and necessary…
Identifying Direct Causes using Intervened Target Variable
Kang Du, Yu Xiang, Ilya Soloveychik
Identifying the direct causes or causal parents of a target variable is crucial for scientific discovery. Focusing on linear models, the invariant prediction framework was built up…
Generalized Invariant Matching Property via LASSO
Kang Du, Yu Xiang
Learning under distribution shifts is a challenging task. One principled approach is to exploit the invariance principle via the structural causal models. However, the invariance p…
Learning Invariant Representations under General Interventions on the Response
Kang Du, Yu Xiang
It has become increasingly common nowadays to collect observations of feature and response pairs from different environments. As a consequence, one has to apply learned predictors…
An Invariant Matching Property for Distribution Generalization under Intervened Response
Kang Du, Yu Xiang
The task of distribution generalization concerns making reliable prediction of a response in unseen environments. The structural causal models are shown to be useful to model distr…
Causal Inference from Slowly Varying Nonstationary Processes
Kang Du, Yu Xiang
Causal inference from observational data following the restricted structural causal model (SCM) framework hinges largely on the asymmetry between cause and effect from the data gen…