mtlgmm
Unsupervised Multi-Task and Transfer Learning on Gaussian Mixture Models
Description
Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning. We propose a multi-task GMM learning procedure based on the Expectation-Maximization (EM) algorithm that not only can effectively utilize unknown similarity between related tasks but is also robust against a fraction of outlier tasks from arbitrary sources. The proposed procedure is shown to achieve minimax optimal rate of convergence for both parameter estimation error and the excess mis-clustering error, in a wide range of regimes. Moreover, we generalize our approach to tackle the problem of transfer learning for GMMs, where similar theoretical results are derived. Finally, we demonstrate the effectiveness of our methods through simulations and a real data analysis. To the best of our knowledge, this is the first work studying multi-task and transfer learning on GMMs with theoretical guarantees. This package implements the algorithms proposed in Tian, Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>.
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CRAN Check Status
Show all 13 flavors
| Flavor | Status |
|---|---|
| r-devel-linux-x86_64-debian-clang | NOTE |
| r-devel-linux-x86_64-debian-gcc | NOTE |
| r-devel-linux-x86_64-fedora-clang | OK |
| r-devel-linux-x86_64-fedora-gcc | OK |
| r-devel-windows-x86_64 | OK |
| r-oldrel-macos-arm64 | OK |
| r-oldrel-macos-x86_64 | OK |
| r-oldrel-windows-x86_64 | OK |
| r-patched-linux-x86_64 | OK |
| r-release-linux-x86_64 | OK |
| r-release-macos-arm64 | OK |
| r-release-macos-x86_64 | OK |
| r-release-windows-x86_64 | OK |
Check details (2 non-OK)
CRAN incoming feasibility
Maintainer: ‘Ye Tian <ye.t@columbia.edu>’ The Description field contains Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
CRAN incoming feasibility
Maintainer: ‘Ye Tian <ye.t@columbia.edu>’ The Description field contains Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
Check History
NOTE 11 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Jun 9, 2026
CRAN incoming feasibility
Maintainer: ‘Ye Tian <ye.t@columbia.edu>’ The Description field contains Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
CRAN incoming feasibility
Maintainer: ‘Ye Tian <ye.t@columbia.edu>’ The Description field contains Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
ERROR 11 OK · 1 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Jun 8, 2026
CRAN incoming feasibility
Maintainer: ‘Ye Tian <ye.t@columbia.edu>’ The Description field contains Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
CRAN incoming feasibility
Maintainer: ‘Ye Tian <ye.t@columbia.edu>’ The Description field contains Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
NOTE 11 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE May 2, 2026
CRAN incoming feasibility
Maintainer: ‘Ye Tian <ye.t@columbia.edu>’ The Description field contains Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
CRAN incoming feasibility
Maintainer: ‘Ye Tian <ye.t@columbia.edu>’ The Description field contains Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
ERROR 9 OK · 2 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Apr 25, 2026
CRAN incoming feasibility
Maintainer: ‘Ye Tian <ye.t@columbia.edu>’ The Description field contains Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
CRAN incoming feasibility
Maintainer: ‘Ye Tian <ye.t@columbia.edu>’ The Description field contains Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
package dependencies
Package required but not available: ‘caret’ See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’ manual.
NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
CRAN incoming feasibility
Maintainer: ‘Ye Tian <ye.t@columbia.edu>’ The Description field contains Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
CRAN incoming feasibility
Maintainer: ‘Ye Tian <ye.t@columbia.edu>’ The Description field contains Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
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Version History
1 trackedR Observatory began tracking this package on Mar 10, 2026; it first appeared on CRAN Oct 31, 2022. Releases before tracking aren’t shown.