Manuscript, 2023
This paper gives a unified statistical guarantee for both underparametrized and overparametrized two-layer ReLU networks, and further reproduces the double descent phenonmenon. Full paper available for download.
Wang, H., & Lin, W. (2023). "Nonasymptotic theory for two-layer neural networks: Beyond the bias–variance trade-off." Manuscript.
Manuscript, 2023
This paper introduces FedADMM, a new federated learning approach for parameter estimation considering heterogeneity in distribution, communication, and accessibility among an exceedingly large number of devices. Full paper available for download.
Wang, H., Zhao, X., & Lin, W. (2023). "Heterogeneous federated learning on arbitrary graphs." Manuscript.
COLT, 2023
This paper explores the learning limit for heterogeneous federated learning. Full paper available for download.
Zhao, X., Wang, H., & Lin, W. (2023). "The aggregation–heterogeneity trade-off in federated learning." The 36th Annual Conference on Learning Theory. To appear.
ICML, 2023
This paper combines tree-based methods with Difference-in-Differences to estimate heterogeneous treatment effects in the presence of unmeasured confounding. Full paper available for download.
Tang, C., Wang, H., Li, X., Cui, Q., Zhang, Y. L., Li, L., & Zhou, J. (2022). "Difference-in-Differences meets tree-based methods: Heterogeneous treatment effects estimation with unmeasured confounding." Proceedings of the Fortieth International Conference on Machine Learning, To appear.
NeurIPS, 2022
This paper proposes a new splitting rule for tree-based methods to estimate heterogeneous treatment effects in the presence of unmeasured confounding. Full paper available for download.
Tang, C., Wang, H., Li, X., Cui, Q., Zhang, Y. L., Zhu, F., Zhou, J., & Jiang, L. (2022). "Debiased causal tree: heterogeneous treatment effects estimation with unmeasured confounding." Advances in Neural Information Processing Systems, 35, 5628-5640.