* These authors contributed equally to the manuscript (their names are sorted alphabetically).
# Students supervised by me.
[19] Ding, L.*, Hu, T.*, Jiang, J.*#, Li, D.*, Wang, W.*, & Yao, Y.* (2024). Random smoothing regularization in kernel gradient descent learning. Journal of Machine Learning Research, accepted.
[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
[20] Liu, R.#, Wang, W., Zhang, C., & Yao, Y. (2025). Invertible TabMap: An Invertible Self-supervised Mapping for Imbalanced Classification of Tabular Data. International Joint Conference on Neural Networks (IJCNN), 2025.
[19] Fang, L.#, Liu, R.#, Zhang, J.#, Wang, W., & Jing, B. Y. (2025). Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning. The Thirteenth International Conference on Learning Representations (ICLR).
[18] Li, S.*, Zhang, Y.*#, Li, W., Chen, H., Wang, W., Jing, B. Y., Lin, S. & Hu, J. (2025). Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution. The Thirteenth International Conference on Learning Representations (ICLR). (Spotlight).
[17] Zhang, Y.#, Li, W., Li, S., Chen, H., Tu, Z., Wang, W., Jing, B. Y., Lin, S. & Hu, J. (2025). AugKD: Ingenious Augmentations Empower Knowledge Distillation for Image Super-Resolution. The Thirteenth International Conference on Learning Representations (ICLR).
[16] Liu, R.#, Fang, L.#, Wang, W., & Jing, B. Y. (2024). D2R2: Diffusion-based Representation with Random Distance Matching for Tabular Few-shot Learning. Neural Information Processing Systems (NeurIPS), 2024.
[15] Zhang, J.#, Fang, L.#, Shi, K. #, Wang, W., & Jing, B. Y. (2024). Q-Distribution guided Q-learning for offline reinforcement learning: Uncertainty penalized Q-value via consistency model. Neural Information Processing Systems (NeurIPS), 2024.
[14] Ma, J.*#, Xue, S.*, Hu, T., Wang, W., Liu, Z., Li, Z., Ma, Z.M., & Kawaguchi, K. (2024). The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling. The Forty-first International Conference on Machine Learning (ICML).
[13] Liu, X., Hu, T., Wang, W., Kawaguchi, K., & Yao, Y. (2024). Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion. The Forty-first International Conference on Machine Learning (ICML).
[12] Ma, J.#, Hu, T., & Wang, W. (2024). Deciphering the projection head: Representation evaluation self-supervised learning. International Joint Conference on Artificial Intelligence (IJCAI).
[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.
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