Hierarchical Mixtures-of-Experts for Exponential Family Regression Models with Generalized Linear Mean Functions: A Survey of Approximation and Consistency Results. - Wenxin Jiang 003; Martin A. Tanner (UAI, 1998)

Hierarchical mixtures-of-experts for generalized linear models: some results on denseness and consistency. - Wenxin Jiang 003; Martin A. Tanner (AISTATS, 1999)

On the Approximation Rate of Hierarchical Mixtures-of-Experts for Generalized Linear Models. - Wenxin Jiang 003; Martin A. Tanner (Neural Comput., 1999)

On the identifiability of mixtures-of-experts. - Wenxin Jiang 003; Martin A. Tanner (Neural Networks, 1999)

Some Results on Weakly Accurate Base Learners for Boosting Regression and Classification. - Wenxin Jiang 003 (Multiple Classifier Systems, 2000)

The VC Dimension for Mixtures of Binary Classifiers. - Wenxin Jiang 003 (Neural Comput., 2000)

On the asymptotic normality of hierarchical mixtures-of-experts for generalized linear models. - Wenxin Jiang 003; Martin A. Tanner (IEEE Trans. Inf. Theory, 2000)

Is regularization unnecessary for boosting?. - Wenxin Jiang 003 (AISTATS, 2001)

Some Theoretical Aspects of Boosting in the Presence of Noisy Data. - Wenxin Jiang 003 (ICML, 2001)

Factorial Hidden Markov Models and the Generalized Backfitting Algorithm. - Robert A. Jacobs; Wenxin Jiang 003; Martin A. Tanner (Neural Comput., 2002)

Boosting with Noisy Data: Some Views from Statistical Theory. - Wenxin Jiang 003 (Neural Comput., 2004)

A note on mixtures of experts for multiclass responses: approximation rate and Consistent Bayesian Inference. - Yang Ge; Wenxin Jiang 003 (ICML, 2006)

On Consistency of Bayesian Inference with Mixtures of Logistic Regression. - Yang Ge; Wenxin Jiang 003 (Neural Comput., 2006)

On the Consistency of Bayesian Variable Selection for High Dimensional Binary Regression and Classification. - Wenxin Jiang 003 (Neural Comput., 2006)

On Uniform Deviations of General Empirical Risks with Unboundedness, Dependence, and High Dimensionality. - Wenxin Jiang 003 (J. Mach. Learn. Res., 2009)

Predicting Panel Data Binary Choice with the Gibbs Posterior. - Lili Yao; Wenxin Jiang 003; Martin A. Tanner (Neural Comput., 2011)

On Convergence Rates of Mixtures of Polynomial Experts. - Eduardo F. Mendes; Wenxin Jiang 003 (Neural Comput., 2012)

General Oracle Inequalities for Gibbs Posterior with Application to Ranking. - Cheng Li; Wenxin Jiang 003; Martin A. Tanner (COLT, 2013)

Generalized Gini Correlation and its Application in Data-Mining. - Yi Gao; Wenxin Jiang 003; Martin A. Tanner (Data Min. Knowl. Discov., 2016)

On oracle property and asymptotic validity of Bayesian generalized method of moments. - Cheng Li; Wenxin Jiang 003 (J. Multivar. Anal., 2016)

Ultra-high dimensional variable selection with application to normative aging study: DNA methylation and metabolic syndrome. - Grace Yoon; Yinan Zheng; Zhou Zhang 002; Haixiang Zhang; Tao Gao; Brian Joyce; Wei Zhang 253; Weihua Guan; Andrea A. Baccarelli; Wenxin Jiang 003; Joel Schwartz; Pantel Vokonas; et al. (BMC Bioinform., 2017)

Bayesian Complex Network Community Detection Using Nonparametric Topic Model. - Ruimin Zhu; Wenxin Jiang 003 (COMPLEX NETWORKS (1), 2018)

Multi-sense Definition Modeling using Word Sense Decompositions. - Ruimin Zhu; Thanapon Noraset; Alisa Liu; Wenxin Jiang 003; Doug Downey (CoRR, 2019)

Statistical Formulas for F Measures. - Wenxin Jiang 003 (CoRR, 2020)

Including a Nugget Effect in Lifted Brownian Covariance Models. - Yang Yu 078; Ning Zhang; Daniel W. Apley; Wenxin Jiang 003 (SIAM/ASA J. Uncertain. Quantification, 2020)

A Note on Comparison of F-measures. - Wei Ju; Wenxin Jiang 003 (CoRR, 2021)

Extended graphical lasso for multiple interaction networks for high dimensional omics data. - Yang Xu; Hongmei Jiang; Wenxin Jiang 003 (PLoS Comput. Biol., 2021)