---
title: "oseledets/TT-Toolbox: papers citing this GitHub repository"
description: "5 papers in the tracked journals link to the GitHub repository oseledets/TT-Toolbox in their Crossref records, the first in 2019; most of them (3) in 2025."
url: https://scholar4ai.com/code/github-oseledets-tt-toolbox
markdown_url: https://scholar4ai.com/code/github-oseledets-tt-toolbox.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "5 papers in the tracked journals link to the GitHub repository oseledets/TT-Toolbox in their Crossref records, the first in 2019; most of them (3) in 2025. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# oseledets/TT-Toolbox: papers linking to it

Research papers in the 480 tracked journals that link to oseledets/TT-Toolbox (https://github.com/oseledets/TT-Toolbox), 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.

- GitHub
- 223 stars
- 75 forks
- MATLAB
- created 2011
- https://github.com/oseledets/TT-Toolbox

All repositories: https://scholar4ai.com/code.md

## Key findings

- 5 papers in the tracked journals link to the GitHub repository oseledets/TT-Toolbox in their Crossref records, the first in 2019; most of them (3) in 2025.
- By category: Operations Research (2), Statistics (2) and AI (1); the journals with the most are Statistics and Computing (2), Computational Optimization and Applications (1) and Optimization and Engineering (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links oseledets/TT-Toolbox.

## By year

*Papers linking to oseledets/TT-Toolbox by publication year*

| Year | Papers |
|---|---:|
| 2019 | 1 |
| 2024 | 1 |
| 2025 | 3 |

## Journals

*Journals whose papers link to oseledets/TT-Toolbox most*

| Journal | Papers |
|---|---:|
| [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 2 |
| [Computational Optimization and Applications](https://scholar4ai.com/journals/computational-optimization-and-applications.md) | 1 |
| [Optimization and Engineering](https://scholar4ai.com/journals/optimization-and-engineering.md) | 1 |
| [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Approximation and sampling of multivariate probability distributions in the tensor train decomposition](https://doi.org/10.1007/s11222-019-09910-z) (Statistics and Computing, 2019)
  - [Tensor train approximation of multivariate Gaussian density by scaling and squaring](https://doi.org/10.1007/s11222-025-10707-6) (Statistics and Computing, 2025) — cites the paper above

## Papers

*Papers linking to oseledets/TT-Toolbox, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2019-11-02 | [Approximation and sampling of multivariate probability distributions in the tensor train decomposition](https://doi.org/10.1007/s11222-019-09910-z) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Sergey Dolgov, Karim Anaya‐Izquierdo, Colin Fox et al. | 61 | cites it |
| 2024-10-14 | [Compact lossy compression of tensors via neural tensor-train decomposition](https://doi.org/10.1007/s10115-024-02252-x) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Taehyung Kwon, Jihoon Ko, Jinhong Jung et al. | 2 | cites it |
| 2025-12-06 | [Tensor train solution to uncertain optimization problems with shared sparsity penalty](https://doi.org/10.1007/s11081-025-10036-7) | [Optimization and Engineering](https://scholar4ai.com/journals/optimization-and-engineering.md) | Harbir Antil, Sergey Dolgov, Akwum Onwunta | 1 | cites it |
| 2025-07-12 | [Efficient training of Gaussian processes with tensor product structure](https://doi.org/10.1007/s10589-025-00707-7) | [Computational Optimization and Applications](https://scholar4ai.com/journals/computational-optimization-and-applications.md) | Josie Koenig, Max Pfeffer, Martin Stoll | 0 | cites it |
| 2025-09-02 | [Tensor train approximation of multivariate Gaussian density by scaling and squaring](https://doi.org/10.1007/s11222-025-10707-6) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Jiří Ajgl, Ondřej Straka | 0 | 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). oseledets/TT-Toolbox: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-oseledets-tt-toolbox

```bibtex
@misc{scholar4ai-code-github-oseledets-tt-toolbox,
  author = {{Scholar4AI}},
  title = {oseledets/TT-Toolbox: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-oseledets-tt-toolbox}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).