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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Convergence of Gradient EM on Multi-component Mixture of Gaussians
Authors: Bowei Yan, Mingzhang Yin, Purnamrita Sarkar
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we collect some numerical results. |
| Researcher Affiliation | Academia | Bowei Yan University of Texas at Austin EMAIL Mingzhang Yin University of Texas at Austin EMAIL Purnamrita Sarkar University of Texas at Austin EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for its methodology. |
| Open Datasets | No | The paper mentions using 'N = 12, 000 data points' but does not specify a publicly available dataset by name, citation, or link. |
| Dataset Splits | No | The paper mentions 'N = 12,000 data points' but does not provide specific dataset split information (e.g., percentages, sample counts, or methodology for splitting). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | In all experiments we set the covariance matrix for each mixture component as identity matrix Id and deο¬ne signal-to-noise ratio (SNR) as Rmin. ... For this set of experiments, we use a mixture of 3 Gaussians in 2 dimensions. In both experiments Rmax/Rmin = 1.5. In different settings of Ο, we apply gradient EM with varying SNR from 1 to 5. For each choice of SNR, we perform 10 independent trials with N = 12, 000 data points. |