Resources

Here are some external resources this project is based on.

Talks

• Sparse Cholesky factorization by Kullback-Leibler minimization (26 minutes)
• Sparse Cholesky factorization by Kullback-Leibler minimization (Florian T. Schäfer) (52 minutes)
• Thesis Defense: Florian Schäfer (44 minutes)

Slides

• Sparse Cholesky Factorization by Greedy Conditional Selection
• Sparse Cholesky Factorization by Greedy Conditional Selection (poster)
• Sparse Cholesky Factorization by Greedy Conditional Selection (blog post)

Papers

[BibTeX]

1. F. Schäfer, M. Katzfuss, and H. Owhadi, "Sparse Cholesky factorization by Kullback-Leibler minimization," arXiv:2004.14455 [cs, math, stat], Oct. 2021, Accessed: Feb. 23, 2022. [Online]. Available: https://arxiv.org/abs/2004.14455
2. F. Schäfer, T. J. Sullivan, and H. Owhadi, "Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity," arXiv:1706.02205 [cs, math], Oct. 2020, Accessed: Feb. 23, 2022. [Online]. Available: https://arxiv.org/abs/1706.02205
3. S. Huan, J. Guinness, M. Katzfuss, H. Owhadi, and F. Schäfer, "Sparse Cholesky factorization by greedy conditional selection." arXiv, Jul. 2023. doi: 10.48550/arXiv.2307.11648.
4. R. B. Gramacy and D. W. Apley, "Local Gaussian process approximation for large computer experiments." arXiv, Oct. 2014. Accessed: Jun. 26, 2022. [Online]. Available: https://arxiv.org/abs/1303.0383
5. M. Kang and M. Katzfuss, "Correlation-based sparse inverse Cholesky factorization for fast Gaussian-process inference." arXiv, Dec. 2021. Accessed: Jun. 25, 2022. [Online]. Available: https://arxiv.org/abs/2112.14591
6. H. H. Bajgiran et al., "Uncertainty quantification of the 4th kind; optimal posterior accuracy-uncertainty tradeoff with the minimum enclosing ball," Journal of Computational Physics, vol. 471, p. 111608, Dec. 2022, doi: 10.1016/j.jcp.2022.111608.
7. H. Owhadi and G. R. Yoo, "Kernel Flows: From learning kernels from data into the abyss," Journal of Computational Physics, vol. 389, pp. 22–47, Jul. 2019, doi: 10.1016/j.jcp.2019.03.040.
8. G. Lavrentiadis et al., "Overview and introduction to development of non-ergodic earthquake ground-motion models," Bulletin of Earthquake Engineering, Aug. 2022, doi: 10.1007/s10518-022-01485-x.
9. G. Lavrentiadis, N. A. Abrahamson, and N. M. Kuehn, "A non-ergodic effective amplitude ground-motion model for California," Bulletin of Earthquake Engineering, Sep. 2021, doi: 10.1007/s10518-021-01206-w.