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A discussion on “A selective review of statistical methods using calibration information from similar studies”
Ling Zhou ,
Southwestern University of Finance and Economics, FMCYNAMEChengdu, People's Republic of China
Peter X.-K. Song
University of Michigan, Ann Arbor, MI, USA
pxsong@umich.edu
Pages |
Received 30 Apr. 2022,
Accepted 02 May. 2022,
Published online: 10 Jun. 2022,
- Abstract
- Full Article
- References
- Citations
- Collins, L., Hassani, H., Mokhtari, A., & Shakkottai, S. (2021). Exploiting shared representations for personalized federated learning. Proceedings of the 38th International Conference on Machine Learning, PMLR 139 (pp. 2089–2099). https://proceedings.mlr.press/v139/collins21a.html
- Fallah, A., Mokhtari, A., & Ozdaglar, A. (2020). Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach. Advances in Neural Information Processing Systems, 33, 3557–3568. https://proceedings.neurips.cc/paper/2020/file/24389bfe4fe2eba8bf9aa9203a44cdad-Paper.pdf
- Hector, E. C., & Song, P. X.-K. (2020). Doubly distributed supervised learning and inference with high-dimensional correlated outcomes. Journal of Machine Learning Research, 21(173), 1–35. http://jmlr.org/papers/v21/19-996.html
- Hector, E. C., & Song, P. X.-K. (2021). A distributed and integrated method of moments for high-dimensional correlated data analysis. Journal of the American Statistical Association, 116(534), 805–818. https://doi.org/10.1080/01621459.2020.1736082
- Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2020). Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems, 2, 429–450. https://proceedings.mlsys.org/paper/2020/file/38af86134b65d0f10fe33d30dd76442e-Paper.pdf
- Luo, L., Zhou, L., & Song, P. X.-K. (2022). Real-time regression analysis of streaming clustered data with possible abnormal data batches. Journal of the American Statistical Association, 1–42. To appear. https://doi.org/10.1080/01621459.2022.2026778
- McMahan, B., Moore, E., Ramage, D., & Hampson, S. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial intelligence and statistics, PMLR (pp. 1273–1282), Lauderdale, FL, USA. https://proceedings.mlr.press/v54/mcmahan17a.html
- Smith, V., Chiang, C.-K., Sanjabi, M., & Talwalkar, A. S. (2017). Federated multi-task learning. Advances in Neural Information Processing Systems, Curran Associates, Inc. (vol. 30), Long Beach, CA, USA. https://proceedings.neurips.cc/paper/2017/file/6211080fa89981f66b1a0c9d55c61d0f-Paper.pdf
- Tang, L., & Song, P. X. (2016). Fused lasso approach in regression coefficients clustering: Learning parameter heterogeneity in data integration. The Journal of Machine Learning Research, 17(113), 3915–3937. http://jmlr.org/papers/v17/15-598.html
- Tang, L., Zhou, L., & Song, P. X.-K. (2020). Distributed simultaneous inference in generalized linear models via confidence distribution. Journal of Multivariate Analysis, 176, Article 104567. https://doi.org/10.1016/j.jmva.2019.104567
- Wang, F., Wang, L., & Song, P. X.-K. (2012). Quadratic inference function approach to merging longitudinal studies: Validation and joint estimation. Biometrika, 99(3), 755–762. https://doi.org/10.1093/biomet/ass021
- Zhou, L., She, X., & Song, P. X.-K. (2022). Distributed empirical likelihood approach to integrating unbalanced datasets. Statistica Sinica. https://doi.org/10.5705/ss.202020.0330
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