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..
Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities
Authors: Wei Qian, Yuqian Zhang, Yudong Chen
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 14,364 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,752 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, empirical results suggest that the log-convexity assumption cannot be relaxed completely: Figure 1 provides an example where the LS-EM algorithm may converge to 0 (an undesired solution) with constant probability when the log-concavity property is violated. See Appendix H for additional numerical results. |
| Researcher Affiliation | Academia | Wei Qian, Yuqian Zhang, Yudong Chen School of Operations Research and Information Engineering Cornell University |
| Pseudocode | No | The paper describes the LS-EM algorithm steps in text in Section 3 but does not provide a formal algorithm box or pseudocode block. |
| Open Source Code | No | No statement regarding the release of open-source code for the described methodology was found. |
| Open Datasets | No | The paper analyzes the algorithm under finite sample settings but does not refer to the use of a specific, publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (training, validation, test) as it focuses on theoretical analysis, with limited empirical results not detailing data splits. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned for replicating the experiments. |
| Experiment Setup | No | While numerical experiments are mentioned in the paper, specific experimental setup details such as hyperparameter values, training configurations, or system-level settings are not provided in the main text. |