Estimating Mixture Models via Mixtures of Polynomials
Authors: Sida Wang, Arun Tejasvi Chaganty, Percy S. Liang
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations show good empirical performance on several models. In Table 3, we show the relative error maxk || k k||2 averaged over 10 random models of each class. |
| Researcher Affiliation | Academia | Sida I. Wang Arun Tejasvi Chaganty Percy Liang Computer Science Department, Stanford University, Stanford, CA, 94305 {sidaw,chaganty,pliang}@cs.stanford.edu |
| Pseudocode | No | The paper includes Figure 1, which provides an overview of the framework with steps, but it is not a formal pseudocode block or algorithm presented in a code-like format or explicitly labeled as 'Algorithm' or 'Pseudocode'. |
| Open Source Code | Yes | We implemented Polymom for several mixture models in Python (code: https://github. com/sidaw/polymom). |
| Open Datasets | No | The paper refers to 'T' as the number of samples and describes various model types (e.g., spherical Gaussians, linear regressions), but it does not specify or provide access information (link, DOI, formal citation) for any publicly available or open datasets used for training. |
| Dataset Splits | No | The paper mentions 'T is the number of samples' but does not provide specific dataset split information, such as exact percentages, sample counts, or citations to predefined splits, needed to reproduce the data partitioning for training, validation, or testing. |
| Hardware Specification | No | The paper states it used 'CVXOPT' and 'Python' for implementation, but it does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Python' and 'CVXOPT' as software used, but it does not provide specific version numbers for these ancillary software components, which are necessary for full reproducibility. |
| Experiment Setup | Yes | The paper specifies experimental setup details for baselines, such as 'EM: sklearn initialized with k-means using 5 random restarts'. |