Lecture notes on high-dimensional data
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Abstract (arXiv)
These are lecture notes based on the first part of a course on 'Mathematical Data Science', which I taught to final year BSc students in the UK in 2019-2020. Topics include: concentration of measure in high dimensions; Gaussian random vectors in high dimensions; random projections; separation/disentangling of Gaussian data. A revised version has been published as part of the textbook [Mathematical Introduction to Data Science, Springer, Berlin, Heidelberg, 2024, https://link.springer.com/book/10.1007/978-3-662-69426-8].
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jan 2021.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 0; 2026: 1.
5 papers.
- 2024A Geometric Explanation of the Likelihood OOD Detection Paradoxcited by 22
- 2022TetraDiffusion: Tetrahedral Diffusion Models for 3D Shape Generationcited by 14
- 2026InfoNCE Induces Gaussian Distributioncited by 14
- 2024Towards Compositionality in Concept Learningcited by 7
- 2023SQ Lower Bounds for Learning Bounded Covariance GMMscited by 0
- 2026InfoNCE Induces Gaussian Distribution27 Feb 2026
- 2024Towards Compositionality in Concept Learning26 Jun 2024
- 2024A Geometric Explanation of the Likelihood OOD Detection Paradox27 Mar 2024
- 2023SQ Lower Bounds for Learning Bounded Covariance GMMs22 Jun 2023
- 2022TetraDiffusion: Tetrahedral Diffusion Models for 3D Shape Generation23 Nov 2022