Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Regret Bounds for Multilabel Classification in Sparse Label Regimes
Authors: Róbert Busa-Fekete, Heejin Choi, Krzysztof Dembczynski, Claudio Gentile, Henry Reeve, Balazs Szorenyi
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 19,142 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,143 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We fill the gap in the landscape of theoretical results by providing upper and lower finite-sample regret bounds in MLC with a focus on computational efficiency. We consider two learning setups, a nonparametric and a parametric one. ... As a last contribution, we derive MLC regret lower bounds for our MLC setups revealing that, at least in the non-parametric case, our upper bound for Hamming loss is optimal up to a log s factor, and that our regret upper bound for Precision@κ is optimal up to a log m factor. ... No experimential results. |
| Researcher Affiliation | Collaboration | Róbert Busa-Fekete Google Research EMAIL Heejin Choi Google EMAIL Krzysztof Dembczy nski Yahoo Research Poznan University of Technology EMAIL Claudio Gentile Google Research EMAIL Henry W. Reeve University of Bristol EMAIL Balázs Szörényi Yahoo Research EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] No experimential results. |
| Open Datasets | No | No experimential results. ... No data provided. |
| Dataset Splits | No | No experimential results. |
| Hardware Specification | No | No experimential results. |
| Software Dependencies | No | The paper is theoretical and does not report any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | No experimential results. |