5 citations · 5 across the 2 of their papers we have counts for
5 papers
Confounder Balancing for Instrumental Variable Regression with Latent Variable
Anpeng Wu, Kun Kuang, Ruoxuan Xiong +2
This paper studies the confounding effects from the unmeasured confounders and the imbalance of observed confounders in IV regression and aims at unbiased causal effect estimation.…
Stable Prediction with Model Misspecification and Agnostic Distribution Shift
Kun Kuang, Ruoxuan Xiong, Peng Cui +2
For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training dat…
Stable Prediction across Unknown Environments
Kun Kuang, Ruoxuan Xiong, Peng Cui +2
In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the testing distribution on which the classifier wi…
State-Varying Factor Models of Large Dimensions
Markus Pelger, Ruoxuan Xiong
This paper develops an inferential theory for state-varying factor models of large dimensions. Unlike constant factor models, loadings are general functions of some recurrent state…
Interpretable Sparse Proximate Factors for Large Dimensions
Markus Pelger, Ruoxuan Xiong
This paper proposes sparse and easy-to-interpret proximate factors to approximate statistical latent factors. Latent factors in a large-dimensional factor model can be estimated by…