The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Subset Ranking Using Regression
Overview
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Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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Self-check
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Citations
Cites
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Learning to rank using gradient descent
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102363 · Cited by 2,836
Cited by
Learning to rank: from pairwise approach to listwise approach
Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., & Li, H. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273513 · Cited by 1,960
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs
Ma, K., Liu, W., Liu, T., Wang, Z., & Tao, D. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2708503 · Cited by 353
Ordinal hyperplanes ranker with cost sensitivities for age estimation
Chang, K.-Y., Chen, C.-S., & Hung, Y.-P. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995437 · Cited by 342
Gulwani, S., Polozov, O., & Singh, R. (2017) · Foundations and Trends in Programming Languages · doi:10.1561/2500000010 · Cited by 319
Ranking and Empirical Minimization of U-statistics
Clémençon, S., Lugosi, G., & Vayatis, N. (2008) · The Annals of Statistics · doi:10.1214/009052607000000910 · Cited by 275
Metadata
- Authors
- Cossock, D., & Zhang, T.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2006
- Pages
- 605-619
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1708221419
- Cited by
- 236
- References
- 2
- FWCI
- 8.21
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Subset Ranking Using Regression. https://app.cochat.ai/papers/10.1007/11776420_44
- The paper
- Cossock, D., & Zhang, T. (2006). Subset Ranking Using Regression. Lecture Notes in Computer Science. https://doi.org/10.1007/11776420_44
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
