| On Prediction Properties of Kriging: Uniform Error Bounds and Robustness W Wang, R Tuo, CJ Wu Journal of the American Statistical Association, 1-38, 2019 | 69 | 2019 |
| Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network T Hu*, W Wang*, C Lin, G Cheng, (*equal contributions) the 24th International Conference on Artificial Intelligence and Statistics, 2021 | 53 | 2021 |
| Deep learning for multivariate time series imputation: A survey J Wang, W Du, W Cao, K Zhang, W Wang, Y Liang, Q Wen arXiv preprint arXiv:2402.04059, 2024 | 43 | 2024 |
| Kriging prediction with isotropic Mat\'ern correlations: robustness and experimental design R Tuo*, W Wang*, (*equal contributions) J. Mach. Learn. Res. 21, 2019 | 39 | 2019 |
| Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression W Wang, BY Jing Journal of machine learning research 23 (193), 1-67, 2022 | 37* | 2022 |
| Your contrastive learning is secretly doing stochastic neighbor embedding T Hu, Z Liu, F Zhou, W Wang, W Huang arXiv preprint arXiv:2205.14814, 2022 | 25 | 2022 |
| Controlling sources of inaccuracy in stochastic kriging W Wang, B Haaland Technometrics, 2019 | 25 | 2019 |
| Neural network Gaussian process considering input uncertainty for composite structure assembly C Lee, J Wu, W Wang, X Yue IEEE/ASME Transactions on Mechatronics 27 (3), 1267-1277, 2020 | 23 | 2020 |
| On the inference of applying Gaussian process modeling to a deterministic function W Wang Electronic Journal of Statistics 15 (2), 5014-5066, 2021 | 22 | 2021 |
| Solving spatial-fractional partial differential diffusion equations by spectral method N Nie, J Huang, W Wang, Y Tang Journal of Statistical Computation and Simulation 84 (6), 1173-1189, 2014 | 20 | 2014 |
| Understanding Square Loss in Training Overparametrized Neural Network Classifiers T Hu*, J Wang*, W Wang*, Z Li, (*equal contributions) Neural Information Processing Systems 2022, 2022 | 18 | 2022 |
| Complexity matters: Rethinking the latent space for generative modeling T Hu, F Chen, H Wang, J Li, W Wang, J Sun, Z Li Advances in Neural Information Processing Systems 36, 29558-29579, 2023 | 16 | 2023 |
| Gaussian processes with input location error and applications to the composite parts assembly process W Wang, X Yue, B Haaland, CF Jeff Wu SIAM/ASA Journal on Uncertainty Quantification 10 (2), 619-650, 2022 | 15 | 2022 |
| Multi-Resolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments CL Sung*, W Wang*, M Plumlee, B Haaland, (*equal contributions) Journal of the American Statistical Association, 1-32, 2019 | 15 | 2019 |
| Differentiable and scalable generative adversarial models for data imputation Y Wu, J Wang, X Miao, W Wang, J Yin IEEE Transactions on Knowledge and Data Engineering 36 (2), 490-503, 2023 | 13 | 2023 |
| A framework for controlling sources of inaccuracy in Gaussian process emulation of deterministic computer experiments B Haaland, W Wang, V Maheshwari SIAM/ASA Journal on Uncertainty Quantification 6 (2), 497-521, 2018 | 13 | 2018 |
| Counterclr: Counterfactual contrastive learning with non-random missing data in recommendation J Wang, H Li, C Zhang, D Liang, E Yu, W Ou, W Wang 2023 IEEE International Conference on Data Mining (ICDM), 1355-1360, 2023 | 11 | 2023 |
| Deciphering the projection head: Representation evaluation self-supervised learning J Ma, T Hu, W Wang arXiv preprint arXiv:2301.12189, 2023 | 10 | 2023 |
| Elucidating the design space of classifier-guided diffusion generation J Ma, T Hu, W Wang, J Sun arXiv preprint arXiv:2310.11311, 2023 | 9 | 2023 |
| A compact difference scheme for time fractional diffusion equation with Neumann boundary conditions J Huang, Y Tang, W Wang, J Yang Asian Simulation Conference, 273-284, 2012 | 9 | 2012 |
Tianyang HuPostdoc, National University of SingaporeΗ διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα nus.edu.sg
