Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities
Authors: Wei Qian, Yuqian Zhang, Yudong Chen
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |