tommy / data-science-papers
Papers of interest for data scientists, covering mathematics, scientific computing, optimization, statistics, etc.
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Machine Learning: The High Interest Credit Card of Technical Debt
(research.google)
Added 9 months ago
Machine learning offers a powerful toolkit for building complex systems, but it is easy to incur massive ongoing maintenance costs.
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node2vec: Scalable Feature Learning for Networks
(arxiv.org)
Added 9 months ago
Creating embeddings for nodes (or edges) in networks, that can capture both homophily and structural equivalence.
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Added 9 months ago
by Philip L. Lehman and S. Bing Yao. (1981) This is the B-tree used by PostgreSQL. https://doi.org/10.1145/319628.319663
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Limited Discrepancy Search
(ai.dmi.unibas.ch)
Added 10 months ago
William D. Harvey and Matthew L. Ginsberg
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Graph Algorithms in the Language of Linear Algebra
(sites.cs.ucsb.edu)
Added 10 months ago
Presentation by John R. Gilbert
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Mathematical Foundations of the GraphBLAS
(arxiv.org)
Added 10 months ago
Connects linear algebra and graph theory. Matrix-based graph operations that can be used to implement a graph algorithms.
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Top 10 algorithms in data mining
(icdm.zhonghuapu.com)
Added 10 months ago
Wu, X., Kumar, V., Ross Quinlan, J. et al. Knowl Inf Syst 14, 1–37 (2008). https://doi.org/10.1007/s10115-007-0114-2
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Pen and Paper Exercises in Machine Learning
(arxiv.org)
Added 10 months ago
Michael U. Gutmann (2022), This is a collection of (mostly) pen-and-paper exercises in machine learning.
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Disciplined Convex Programming
(researchgate.net)
Added 10 months ago
Grant, Michael & Boyd, Stephen & Ye, Yinyu. (2006). Disciplined Convex Programming. 10.1007/0-387-30528-9_7.
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Added 10 months ago
Pedersen EJ, Miller DL, Simpson GL, Ross N. 2019. PeerJ 7:e6876 https://doi.org/10.7717/peerj.6876
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Created: April 26, 2025 Updated: November 2, 2025
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