Optimization of Hierarchical Regression Model with Application to Optimizing Multi-Response Regression K-ary Trees Article Swipe
Pooya Tavallali
,
Peyman Tavallali
,
Mukesh Singhal
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v33i01.33015133
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v33i01.33015133
THIS PAPER HAS BEEN RETRACTED The University of California, Merced campus, recently completed a formal investigation into allegations that content in this paper ("Optimization of Hierarchical Regression Model with Application to Optimizing Multi-Response Regression K-ary Trees," in Proceedings of the AAAI Conference on Artificial Intelligence, 2019, vol. 33, pp. 5133– 5142) was not properly attributed to the novel work of Professor Carreira—Perpiñán, reflected in a previously published paper, "Alternating Optimization of Decision Trees, with Application to Learning Sparse Oblique Trees,” published in Advances in Neural Information Processing Systems, 2018, (vol. 31, pp. 1211–1221). The UC Merced investigation was conducted in consultation with NSF, which funded the research reflected in the original Advances in Neural Information Processing Systems paper, and in accordance with UC Merced policies and procedures on research misconduct. The allegations were substantiated by a preponderance of the evidence. The investigation committee found, in pertinent part, that “key novel ideas in the AAAI paper were taken from the NeurIPS paper without appropriate credit." The initial submission of the AAAI paper, upon which the acceptance decision was made, did not include a reference to the NeurIPS paper, and thus represented a clear case of plagiarism. A citation to the NeurIPS paper was added in the final revision, after the reviewers had already decided to accept it, making it impossible for the AAAI peer-review process to consider the contributions of the AAAI article in light of the earlier work reported in the NeurIPS paper. Furthermore, the citation to the NeurIPS paper in this manuscript does not properly credit the original contributions of the NeurIPS paper and how those ideas were reflected in the work reported in this paper. In response to a formal request made by the University of California's Chancellor's office, and after careful review by the AAAI Publications Committee, AAAI has decided to retract this paper. One of the conditions of submission of a paper for publication in AAAI conference proceedings is that authors declare explicitly that their work is original and has not appeared in a publication elsewhere. Reuse of any data should be appropriately cited. As such this paper represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of this proceedings that this was not detected during the submission process.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v33i01.33015133
- https://ojs.aaai.org/index.php/AAAI/article/download/4447/18387
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All OpenAlex metadata
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https://openalex.org/W2905301908Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v33i01.33015133Digital Object Identifier
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Optimization of Hierarchical Regression Model with Application to Optimizing Multi-Response Regression K-ary TreesWork title
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articleOpenAlex work type
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enPrimary language
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2019Year of publication
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2019-07-17Full publication date if available
- Authors
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Pooya Tavallali, Peyman Tavallali, Mukesh SinghalList of authors in order
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https://doi.org/10.1609/aaai.v33i01.33015133Publisher landing page
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https://ojs.aaai.org/index.php/AAAI/article/download/4447/18387Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/4447/18387Direct OA link when available
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Citation, Computer science, Operations research, Artificial intelligence, Machine learning, Regression analysis, Regression, Library science, Engineering, Statistics, MathematicsTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2022: 1, 2020: 1, 2019: 2Per-year citation counts (last 5 years)
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50Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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