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publications
Debiased causal tree: heterogeneous treatment effects estimation with unmeasured confounding
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.
Difference-in-Differences meets tree-based methods: Heterogeneous treatment effects estimation with unmeasured confounding
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.
The aggregation–heterogeneity trade-off in federated learning
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.
Heterogeneous federated learning on arbitrary graphs
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.
Nonasymptotic theory for two-layer neural networks: Beyond the bias–variance trade-off
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.
CARE: Large Precision Matrix Estimation for Compositional Data
JASA, 2024
This paper introduces a new method called CARE (composition adaptive regularized estimation) for estimating sparse precision matrices in high-dimensional compositional data, providing theoretical guarantees and demonstrating its effectiveness in inferring microbial ecological networks.
Zhang, S., Wang, H.,& Lin, W. (2024). "CARE: Large Precision Matrix Estimation for Compositional Data." Journal of the American Statistical Association, April, 1–13. doi:10.1080/01621459.2024.2335586.
talks
CARE: Large precision matrix estimation for compositional data
Published:
I won the Second Prize at the Fourth National Academic Forum for Doctoral Students in Statistics (awarded by the Chinese Association for Applied Statistics).
Heterogeneous Federated Learning on a Graph
Published:
Slides can be found here.
Robust and Efficient High‐dimensional Inference With Surrogate Outcomes
Published:
Slides can be found here.
teaching
Teaching experience 2
Workshop University 1, Department 2015
This is a description of a teaching experience. You can use markdown like any other post.
Statistical Foundations of Machine Learning
1900
- Wang, H., Lin, W. (2023). “Nonasymptotic theory for two-layer neural networks: Beyond the bias–variance trade-off.” Manuscript.
- Wang, H., Zhao, X.,Lin, W. (2023). “Heterogeneous federated learning on arbitrary graphs.” Manuscript.