house-prices-advanced-regression-techniques: papers linking to it

Kaggle · www.kaggle.com/competitions/house-prices-advanced-regression-techniques

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to house-prices-advanced-regression-techniques (https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

3 papers in the tracked journals link to the Kaggle dataset house-prices-advanced-regression-techniques in their Crossref records, the first in 2024; most of them (2) in 2024.
By category: AI (2) and Statistics (1); the journals with the most are Machine Learning (1), Knowledge and Information Systems (1) and Statistics and Computing (1).

By year#

20242025

Papers linking to house-prices-advanced-regression-techniques by publication year
Year	Papers
2024	2
2025	1

Journals#

Journals whose papers link to house-prices-advanced-regression-techniques most
Journal	Papers
Machine Learning	1
Knowledge and Information Systems	1
Statistics and Computing	1

Papers linking to house-prices-advanced-regression-techniques, most cited first
Published	Title	Journal	Authors	Citations	Link
2024-01-05	NuZZ: Numerical Zig-Zag for general models	Statistics and Computing	Filippo Pagani, Augustin Chevallier, Sam Power et al.	8	cites it
2024-07-15	Partitioned least squares	Machine Learning	Roberto Esposito, Mattia Cerrato, Marco Locatelli	4	cites it
2025-07-18	An in-depth analysis of KernelSHAP and SamplingSHAP: assessing robustness, error, and efficiency	Knowledge and Information Systems	Beyza Aydoğan, Tevfik Aytekin	3	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). house-prices-advanced-regression-techniques: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-competition-house-prices-advanced-regression-techniques

@misc{scholar4ai-code-kaggle-competition-house-prices-advanced-regression-techniques,
  author = {{Scholar4AI}},
  title = {house-prices-advanced-regression-techniques: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-competition-house-prices-advanced-regression-techniques}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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