References

Papers introducing the method

Moka, S., Liquet, B., Zhu, H. and Muller, S. (2024). COMBSS: best subset selection via continuous optimization. Statistics and Computing 34, 75. doi:10.1007/s11222-024-10387-8
Mathur, A., Liquet, B., Muller, S. and Moka, S. (2026). Parsimonious Subset Selection for Generalized Linear Models with Biomedical Applications. arXiv preprint arXiv:2603.21952.

COMBSS extensions

Mathur, A., Moka, S. and Botev, Z. (2023). Column Subset Selection and Nyström Approximation via Continuous Optimization. Proceedings of the 2023 Winter Simulation Conference (WSC), 3601–3612. arXiv:2304.09678.
Liquet, B., Moka, S. and Muller, S. (2025). Best subset solution path for linear dimension reduction models using continuous optimization (sparse PCA and sparse PLS). Biometrical Journal 67 (1), e70015. arXiv:2403.20007.
Mathur, A., Moka, S., Liquet, B. and Botev, Z. (2024). Group COMBSS: group selection via continuous optimization. 2024 Winter Simulation Conference (WSC), IEEE, 3217–3228. arXiv:2404.13339.
Reimann, H., Moka, S. and Sofronov, G. (2024). Continuous Optimization for Offline Change Point Detection and Estimation. Proceedings of the 2024 Winter Simulation Conference (WSC).
Moka, S., Quiroz, M., Asimit, V. and Muller, S. (2025). A Scalable Gradient-Based Optimization Framework for Sparse Minimum-Variance Portfolio Selection. arXiv preprint arXiv:2505.10099.

Software

R — combss on CRAN — package page
Python — combss on PyPI — package page
Source for this presentation — github.com/saratmoka/combss-statfest

Datasets used in this presentation

Khan SRBCT. Khan, J. et al. (2001). Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine 7, 673–679. link
Rice GWAS. McCouch, S. R. et al. (2016). Open access resources for genome-wide association mapping in rice. Nature Communications 7, 10532. link

Acknowledgements

The R package is maintained by Benoit Liquet; the Python package is maintained by Sarat Moka. Anant Mathur contributed to the development of both.

← Previous: What’s next

--- title: "References" --- ## Papers introducing the method - Moka, S., Liquet, B., Zhu, H. and Muller, S. (2024). *COMBSS: best subset selection via continuous optimization.* **Statistics and Computing** 34, 75. [doi:10.1007/s11222-024-10387-8](https://doi.org/10.1007/s11222-024-10387-8){target="_blank"} - Mathur, A., Liquet, B., Muller, S. and Moka, S. (2026). *Parsimonious Subset Selection for Generalized Linear Models with Biomedical Applications.* **arXiv preprint** [arXiv:2603.21952](https://arxiv.org/abs/2603.21952){target="_blank"}. ## COMBSS extensions - Mathur, A., Moka, S. and Botev, Z. (2023). *Column Subset Selection and Nyström Approximation via Continuous Optimization.* **Proceedings of the 2023 Winter Simulation Conference (WSC)**, 3601–3612. [arXiv:2304.09678](https://arxiv.org/abs/2304.09678){target="_blank"}. - Liquet, B., Moka, S. and Muller, S. (2025). *Best subset solution path for linear dimension reduction models using continuous optimization* (sparse PCA and sparse PLS). **Biometrical Journal** 67 (1), e70015. [arXiv:2403.20007](https://arxiv.org/abs/2403.20007){target="_blank"}. - Mathur, A., Moka, S., Liquet, B. and Botev, Z. (2024). *Group COMBSS: group selection via continuous optimization.* **2024 Winter Simulation Conference (WSC)**, IEEE, 3217–3228. [arXiv:2404.13339](https://arxiv.org/abs/2404.13339){target="_blank"}. - Reimann, H., Moka, S. and Sofronov, G. (2024). *Continuous Optimization for Offline Change Point Detection and Estimation.* **Proceedings of the 2024 Winter Simulation Conference (WSC)**. - Moka, S., Quiroz, M., Asimit, V. and Muller, S. (2025). *A Scalable Gradient-Based Optimization Framework for Sparse Minimum-Variance Portfolio Selection.* **arXiv preprint** [arXiv:2505.10099](https://arxiv.org/abs/2505.10099){target="_blank"}. ## Software - **R** — `combss` on CRAN — [package page](https://cran.r-project.org/web/packages/combss/index.html){target="_blank"} - **Python** — `combss` on PyPI — [package page](https://pypi.org/project/combss/){target="_blank"} - Source for this presentation — [github.com/saratmoka/combss-statfest](https://github.com/saratmoka/combss-statfest){target="_blank"} ## Datasets used in this presentation - **Khan SRBCT.** Khan, J. et al. (2001). *Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks.* **Nature Medicine** 7, 673–679. [link](https://www.nature.com/articles/nm0601_673){target="_blank"} - **Rice GWAS.** McCouch, S. R. et al. (2016). *Open access resources for genome-wide association mapping in rice.* **Nature Communications** 7, 10532. [link](https://www.nature.com/articles/ncomms10532){target="_blank"} ## Related methods cited - **Lasso.** Tibshirani, R. (1996). *Regression shrinkage and selection via the Lasso.* **Journal of the Royal Statistical Society, Series B** 58 (1), 267–288. - **MIO best subset.** Bertsimas, D., King, A. and Mazumder, R. (2016). *Best subset selection via a modern optimization lens.* **Annals of Statistics** 44 (2), 813–852. - **SCAD.** Fan, J. and Li, R. (2001). *Variable selection via nonconcave penalized likelihood and its oracle properties.* **Journal of the American Statistical Association** 96 (456), 1348–1360. - **MCP.** Zhang, C.-H. (2010). *Nearly unbiased variable selection under minimax concave penalty.* **Annals of Statistics** 38 (2), 894–942. ## Acknowledgements The R package is maintained by Benoit Liquet; the Python package is maintained by Sarat Moka. Anant Mathur contributed to the development of both. ::: {.page-nav} [← Previous: What's next](whats-next.qmd) <span class="nav-spacer"></span> :::