Selected Publications

A selection of journal articles and refereed conference papers. Use the search box to filter by author, title, venue or year.

All Journal Conference

 

Accepted & In Press · 2025–2026

1. 2026
   Regularized Physics-Informed Neural Networks for Parameter Estimation in Differential Equation Models

   Liu, Y., Chun, H. J., and Wang, X.

   TechnometricsAccepted
2. 2026
   Transition Dynamics Modeling of Pre-trained Accelerometry Representations for Time to Diagnosis of Parkinson’s Disease

   Xie, H., Xue, F., and Wang, X.

   Lifetime Data AnalysisAccepted
3. 2025
   Clustered Temporal Decomposition for High-Dimensional Data

   Shin, H. and Wang, X.

   Journal of Agricultural, Biological, and Environmental StatisticsAccepted
4. 2025
   Theoretical Perspectives on Knowledge Distillation: A Review

   Liu, C., Yin, H., and Wang, X.

   WIREs Computational StatisticsAccepted
5. 2025
   Neural Conformal Inference for Jump Diffusion Processes

   Hyun, H. J. and Wang, X.

   Journal of EconometricsAccepted
6. 2025
   Censor Dependent Variational Inference

   Liu, C. and Wang, X.

   International Conference on Machine Learning (ICML) 2025Conference
7. 2025
   Wasserstein Coreset via Sinkhorn Loss

   Yin, H., Qiu, Y., and Wang, X.

   Transactions on Machine Learning Research
8. 2025
   Double Robust Conditional VAE via Decode Calibration: An Implicit KL Annealing Approach

   Liu, C. and Wang, X.

   Transactions on Machine Learning Research
9. 2025
   Fast Cost-Constrained High-Dimensional Regression

   Hyun, H. J. and Wang, X.

   Statistica Sinica

2020 – 2024

1. 2024
   Inductive Global and Local Manifold Approximation and Projection

   Kim, J. and Wang, X.

   Transactions on Machine Learning Research
2. 2024
   Adaptive Learning of the Latent Space of Wasserstein Generative Adversarial Networks

   Qiu, Y., Gao, Q., and Wang, X.

   Journal of the American Statistical Association
3. 2024
   Implicit Generative Prior for Bayesian Neural Networks

   Liu, Y. and Wang, X.

   Annals of Applied Statistics
4. 2023
   Efficient Multimodal Sampling via Tempered Distribution Flow

   Qiu, Y. and Wang, X.

   Journal of the American Statistical Association
5. 2023
   Partial Conditioning for Inference of Many-Normal-Means with Hölder Constraints

   Yang, J., Wang, X., and Liu, C.

   International Journal of Approximate Reasoning
6. 2022
   Robust Sensible Adversarial Learning of Deep Neural Networks for Image Classification

   Kim, J. and Wang, X.

   Annals of Applied Statistics
7. 2021
   Functional Finite Mixture Regression Models

   Wang, X., Liu, Y., and Zhu, H.

   Statistica Sinica
8. 2021
   Inferential Wasserstein Generative Adversarial Networks

   Chen, Y., Gao, Q., and Wang, X.

   Journal of the Royal Statistical Society, Series B
9. 2021
   A Nonlinear Sparse Neural ODE Model for Multiple Functional Processes

   Liu, Y., Li, K., and Wang, X.

   Canadian Journal of Statistics
10. 2021
    Theoretical Investigation of Generalization Bounds for Adversarial Learning of Deep Neural Networks

    Gao, Q. and Wang, X.

    Journal of Statistical Theory and Practice
11. 2020
    High-Dimensional Spatial Quantile Function-on-Scalar Regression

    Zhang, Z., Wang, X., Kong, L., and Zhu, H.

    Journal of the American Statistical AssociationDOI
12. 2020
    Weight Normalized Deep Neural Networks

    Xu, Y. and Wang, X.

    Stat
13. 2020
    Scalable Network Estimation with L0 Penalty

    Kim, J., Zhu, H., Wang, X., and Do, K.

    Statistical Analysis and Data Mining
14. 2020
    ALMOND: Adaptive Latent Modeling and Optimization via Neural Networks and Langevin Diffusion

    Qiu, Y. and Wang, X.

    Journal of the American Statistical AssociationDOI
15. 2020
    Deep Feature Selection via Deep Neural Networks

    Chen, Y., Gao, Q., Liang, F., and Wang, X.

    Journal of Computational and Graphical Statistics2019 ASA SLDS Student Paper Award
16. 2020
    D-CCA: A Decomposition-based Canonical Correlation Analysis for High-Dimensional Datasets

    Shu, H., Wang, X., and Zhu, H.

    Journal of the American Statistical Association 115, 292–306
17. 2020
    Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models

    Qiu, Y. and Wang, X.

    ICLR 2020Spotlight, top 6%
18. 2020
    Stochastic Approximate Gradient Descent via the Langevin Algorithm

    Qiu, Y. and Wang, X.

    AAAI 2020Acceptance 20.6%
19. 2020
    Cross-Lingual Document Retrieval with Smooth Learning

    Liu, J., Zhang, X., Goldwasser, D., and Wang, X.

    COLING 2020Acceptance 32.9%
20. 2020
    A Data Denoising Approach to Optimize Functional Clustering of Single Cell RNA-Sequencing Data

    Wan, C., Jia, D., Zhao, Y., Chang, W., Cao, S., Wang, X., and Zhang, C.

    IEEE BIBM 2020Acceptance 19.4%

2015 – 2019

1. 2019
   Sparse Deep Neural Networks using L_1,∞ Weight Normalization

   Ren, M., Xu, Y., Lin, Y., Yang, Z., and Wang, X.

   Statistica SinicaDOI 10.5705/ss.202018.0468
2. 2019
   Theoretical Investigation of Generalization Bound for Residual Networks

   Mo, Z., Chen, H., Yang, Z., and Wang, X.

   IJCAI 2019Acceptance 17.9%
3. 2018
   Optimal Penalized Function-on-Function Regression under a Reproducing Kernel Hilbert Space

   Sun, X., Pang, D., Wang, X., and Ma, P.

   Journal of the American Statistical Association 113, 1601–1611
4. 2018
   Classification of Short Single Lead Electrocardiograms (ECGs) for Atrial Fibrillation Detection using Piecewise Linear Spline and XGBoost

   Chen, Y., Wang, X., Jung, Y., Abedi, V., Zand, R., Bikak, M., and Adibuzzaman, M.

   Physiological Measurement 39(10):104006
5. 2018
   Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units

   Xu, Y. and Wang, X.

   NeurIPS 2018 130–139Acceptance 20.8%
6. 2018
   On the Statistical Efficiency of Compositional Nonparametric Prediction

   Xu, Y., Jean, F., and Wang, X.

   AISTATS 2018 1531–1539Acceptance 33%
7. 2017
   Minimax Lower Bound and Optimal Estimation of Convex Functions in the Sup-Norm

   Lebair, T., Shen, J., and Wang, X.

   IEEE Transactions on Automatic Control 62, 3482–3487
8. 2017
   Generalized Scalar-on-Image Regression Models via Total Variation

   Wang, X. and Zhu, H.

   Journal of the American Statistical Association 112, 1156–1168
9. 2016
   Optimal Estimation for the Functional Cox Model

   Qu, S., Wang, J. L., and Wang, X.

   Annals of Statistics 44, 1708–1738
10. 2015
    Optimal Prediction in an Additive Functional Model

    Wang, X. and Ruppert, D.

    Statistica Sinica 25, 567–590

2010 – 2014

1. 2014
   Penalized Likelihood Functional Regression

   Du, P. and Wang, X.

   Statistica Sinica 24, 1017–1041
2. 2013
   Uniform Convergence and Rate Adaptive Estimation of Convex Functions via Constrained Optimization

   Wang, X. and Shen, J.

   SIAM Journal on Control and Optimization 51, 2753–2787
3. 2013
   Smoothing Splines with Varying Smoothing Parameter

   Wang, X., Du, P., and Shen, J.

   Biometrika 100, 955–970
4. 2013
   Nonparametric Estimation of Spatial and Space-Time Covariance Function

   Choi, I., Li, B., and Wang, X.

   Journal of Agricultural, Biological, and Environmental Statistics 4, 611–630
5. 2012
   Convex Regression via Penalized Splines: A Complementarity Approach

   Shen, J. and Wang, X.

   American Control Conference, Montreal
6. 2011
   Estimation of Monotone Functions via P-Splines: A Constrained Dynamical Optimization Approach

   Shen, J. and Wang, X.

   SIAM Journal on Control and Optimization 49, 646–671
7. 2011
   Semiparametric Additive Transformation Model under Current Status Data

   Cheng, G. and Wang, X.

   Electronic Journal of Statistics 5, 1735–1764
8. 2011
   On the Asymptotics of Penalized Spline Smoothing

   Wang, X., Shen, J., and Ruppert, D.

   Electronic Journal of Statistics 5, 1–17
9. 2011
   A Constrained Optimal Control Approach to Smoothing Splines

   Shen, J. and Wang, X.

   50th IEEE Conference on Decision and Control 1729–1734
10. 2010
    A Class of Grouped Brunk Estimators and Penalized Spline Estimators for Monotone Regression

    Wang, X. and Shen, J.

    Biometrika 97, 585–601
11. 2010
    An Inverse Gaussian Process Model for Degradation Data

    Wang, X. and Xu, D.

    Technometrics 52, 188–197
12. 2010
    Wiener Processes with Random Effects for Degradation Data

    Wang, X.

    Journal of Multivariate Analysis 101, 340–351
13. 2010
    Estimation of Shape Constrained Functions in Dynamical Systems and its Applications to Genetic Networks

    Shen, J. and Wang, X.

    American Control Conference, Baltimore

2005 – 2009

1. 2009
   Semiparametric Inference on a Class of Wiener Processes

   Wang, X.

   Journal of Time Series Analysis 30, 179–207
2. 2009
   Nonparametric Estimation of the Shape Function in a Gamma Process for Degradation Data

   Wang, X.

   Canadian Journal of Statistics 37, 101–118
3. 2008
   Model-Independent Estimation of Dark Matter Distributions

   Wang, X., Walker, M., Pal, J., Woodroofe, M., and Mateo, M.

   Journal of the American Statistical Association 103, 1070–1084
4. 2008
   A Pseudo-Likelihood Estimation Method for Nonhomogeneous Gamma Process Model with Random Effects

   Wang, X.

   Statistica Sinica 18, 1153–1163
5. 2008
   Bayesian Free-Knot Monotone Cubic Spline Regression

   Wang, X.

   Journal of Computational and Graphical Statistics 17, 373–387
6. 2008
   Isotonic Smoothing Spline Regression

   Wang, X. and Li, F.

   Journal of Computational and Graphical Statistics 17, 21–37
7. 2007
   A Kiefer–Wolfowitz Comparison Theorem for Wicksell’s Problem

   Wang, X. and Woodroofe, M.

   Annals of Statistics 35, 1559–1575
8. 2007
   Velocity Dispersion Profiles of Seven Dwarf Spheroidal Galaxies

   Walker, M., Mateo, M., Olszewski, E., Gnedini, O., Wang, X., Sen, B., and Woodroofe, M.

   Astrophysical Journal Letters 667, L53–L56
9. 2006
   Radial Velocity Dispersion Profile in the Fornax Dwarf Spheroidal Galaxy

   Walker, M., Mateo, M., Olszewski, E., Wang, X., and Woodroofe, M.

   The Astronomical Journal 131, 2114–2139
10. 2005
    Estimating Dark Matter Distributions

    Wang, X., Woodroofe, M., Walker, M., Mateo, M., and Olszewski, E.

    The Astrophysical Journal 626, 145–158