24 citations · 52 across the 9 of their papers we have counts for
9 papers
Simulation-Based Empirical Bayes
Xinwei Shen, Diana Cai, Cheng Zhang +1
Empirical Bayes (EB) performs simultaneous inference across many related latent variables. Classical EB assumes that the likelihood p(x | z) is tractable. In many scientific applic…
Invariant Probabilistic Prediction
Alexander Henzi, Xinwei Shen, Michael Law +1
In recent years, there has been a growing interest in statistical methods that exhibit robust performance under distribution changes between training and test data. While most of t…
Asymptotic Statistical Analysis of -divergence GAN
Xinwei Shen, Kani Chen, Tong Zhang
Generative Adversarial Networks (GANs) have achieved great success in data generation. However, its statistical properties are not fully understood. In this paper, we consider the…
Reframed GES with a Neural Conditional Dependence Measure
Xinwei Shen, Shengyu Zhu, Jiji Zhang +2
In a nonparametric setting, the causal structure is often identifiable only up to Markov equivalence, and for the purpose of causal inference, it is useful to learn a graphical rep…
To ArXiv or not to ArXiv: A Study Quantifying Pros and Cons of Posting Preprints Online
Charvi Rastogi, Ivan Stelmakh, Xinwei Shen +4
Double-blind conferences have engaged in debates over whether to allow authors to post their papers online on arXiv or elsewhere during the review process. Independently, some auth…
Weakly Supervised Disentangled Generative Causal Representation Learning
Xinwei Shen, Furui Liu, Hanze Dong +3
This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method under appropriate supervised information. Unlike existing disentanglement methods that en…
Surprise sampling: improving and extending the local case-control sampling
Xinwei Shen, Kani Chen, Wen Yu
Fithian and Hastie (2014) proposed a new sampling scheme called local case-control (LCC) sampling that achieves stability and efficiency by utilizing a clever adjustment pertained…
CausalVAE: Structured Causal Disentanglement in Variational Autoencoder
Mengyue Yang, Furui Liu, Zhitang Chen +3
Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data. The framework of…
Bidirectional Generative Modeling Using Adversarial Gradient Estimation
Xinwei Shen, Tong Zhang, Kani Chen
This paper considers the general -divergence formulation of bidirectional generative modeling, which includes VAE and BiGAN as special cases. We present a new optimization metho…