PUBLICATIONS

* These authors contributed equally to the manuscript (their names are sorted alphabetically).

Accepted / Published

[18] Zhang, Q., Wang, W. (2024). Sobolev Calibration of Imperfect Computer Models. Journal of the American Statistical Association, accepted.

[17] Sung, C. L.*, Wang, W.*, Ding, L., & Wang, X. (2023). Mesh-clustered Gaussian process emulator for partial differential equation systems. Technometrics, accepted.

[16] Shi, K., Xiong, Y., Wang, Y., Deng, Y., Wang, W., Jing, B.-Y., & Gao, X. (2024+). PractiCPP: A Deep Learning Approach Tailored for Extremely Imbalanced Datasets in Cell-Penetrating Peptide Prediction. Bioinformatics, 40(2), btae058.

[15] Sung, C.-L., Ji, Y., Mak, S., Wang, W., and Tang, T. (2023). Stacking designs: designing multi-fidelity computer experiments with target predictive accuracy. SIAM/ASA Journal on Uncertainty Quantification, 12(1), 157-181.

[14]  Wang, W.*, Wang, Y.*, & Zhang, X.* (2023). Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High Dimensions. Management Science, accepted.

[13]  Wu, Y., Wang, J., Miao, X., Wang, W., & Yin, J. (2023). Differentiable and Scalable Generative Adversarial Models for Data Imputation. IEEE Transactions on Knowledge and Data Engineering, 36(2), 490-503.

[12]  Sung, C.-L.*, Wang, W.*, Cakoni, F., Harris, I., & Hung, Y. (2023). Functional-input Gaussian processes with applications to inverse scattering problems. Statistica Sinica, accepted.

[11]  Wang, W., & Jing, B.-Y. (2022). Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression. Journal of Machine Learning Research, 23(193):1−67, 2022. 

[10]  Wang, W., Yue, X., Haaland, B., & Wu, C. F. J. (2022). Gaussian Process with Input Location Error and Applications to the Composite Parts Assembly Process. SIAM/ASA Journal on Uncertainty Quantification, 10.2 (2022): 619-650.

[9]    Wang, W. (2021). On the Inference of Applying Gaussian Process Modeling to a Deterministic Function. Electronic Journal of Statistics, 15 (2) 5014 - 5066.

[8]    Wang, W., & Zhou, Y.-H. (2021). Eigenvector-Based Sparse Canonical Correlation Analysis: Fast Computation for Estimation of Multiple Canonical Vectors. Journal of Multivariate Analysis, 104781.

[7]    Lee, C., Wu, J., Wang, W., & Yue, X. (2021). Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assembly. IEEE/ASME Transactions on Mechatronics, 27(3), 1267-1277.

[6]    Tuo, R.*, & Wang, W.* (2020). Kriging Prediction with Isotropic Matérn Correlations: Robustness and Experimental Designs.  Journal of Machine Learning Research, 21(187), 1-38. 

[5]    Wang, W., Tuo, R., & Wu, C. F. J. (2020). On Prediction Properties of Kriging: Uniform Error Bounds and Robustness. Journal of the American Statistical Association, 115:530, 920-930, 

[4]    Sung, C.-L.*, Wang, W.*, Plumlee, M., & Haaland, B. (2020). Multi-Resolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments.  Journal of the American Statistical Association, 115:530, 908-919. 

[3]    Wang, W., & Haaland, B. (2019). Controlling Sources of Inaccuracy in Stochastic Kriging. Technometrics, 61(3): 309-321.

[2]    Haaland, B., Wang, W., & Maheshwari, V. (2018). A Framework for Controlling Sources of Inaccuracy in Gaussian Process Emulation of Deterministic Computer Experiments. SIAM/ASA Journal on Uncertainty Quantification, 6(2), 497-521. 

[1]    Nie, N., Huang, J., Wang, W., & Tang, Y. (2014). Solving Spatial-Fractional Partial Differential Diffusion Equations by Spectral Method. Journal of Statistical Computation and Simulation, 84(6), 1173-1189 

Conference

[11] Ma, J., Hu, T., Wang, W., & Sun, J. (2023). Elucidating The Design Space of Classifier-Guided Diffusion Generation. The Twelfth International Conference on Learning Representations (ICLR).

[10]  Zhang, J., Zhang, C., Wang, W., & Jing, B. Y. (2023). Constrained Policy Optimization with Explicit Behavior Density For Offline Reinforcement Learning. Neural Information Processing Systems (NeurIPS), 2023. 

[9]    Hu, T., Chen, F., Wang, H., Li, J., Wang, W., Sun, J., & Li, Z. (2023). Complexity Matters: Rethinking the Latent Space for Generative Modeling. Neural Information Processing Systems (NeurIPS), 2023. (Spotlight).

[8]    Wang, B., Li, J., Liu, Y., Cheng, J., Rong, Y., Wang, W., Tsung, F. (2023). Deep Insights into Noisy Pseudo Labeling on Graph Data. Neural Information Processing Systems (NeurIPS), 2023.

[7]    Wang, J., Li, H., Zhang, C., Liang, D., Yu, E., Ou, W., & Wang, W. (2023). CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation. The IEEE International Conference on Data Mining (ICDM),  2023. 

[6]    Hu, T., Liu, Z., Zhou, F., Wang, W., & Huang, W. (2023). Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding. The Eleventh International Conference on Learning Representations (ICLR).

[5]    Hu, T.*, Wang, J.*, Wang, W.*, & Li, Z. (2022). Understanding Square Loss in Training Overparametrized Neural Network Classifiers. Neural Information Processing Systems (NeurIPS), 2022. (Spotlight).

[4]    Tuo, R.*, & Wang, W.*. (2022). Uncertainty Quantification for Bayesian Optimization. In the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 151:2862-2884, 2022.

[3]    Hu, T.*, Wang, W.*, Lin, C., & Cheng, G. (2021). Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network.  In the 24th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR 130:829-837, 2021.

[2]    Chokshi. T.*, Eo., J.*, Sonico, J.*, Su, A.*, Wang, W.*, Wu, S.*, Zhou, C.*, & Zhu, Y.* (2015) Structured Comparison of Pallet Racks and Gravity Flow Racks. In IIE Annual Conference 2015. Proceedings, pages 1971 - 1980.

[1]     Huang, J., Tang, Y., Wang, W., & Yang, J. (2012). A Compact Difference Scheme for Time Fractional Diffusion Equation with Neumann Boundary Conditions. In Asia Simulation Conference 2012. Proceedings, pages 273-284. 

Google Scholar