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 [1].
Matrix Manifold Optimization for Gaussian Mixtures
Authors: Reshad Hosseini, Suvrit Sra
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have performed numerous experiments to examine effectiveness of our method. Below we report performance comparisons on both real and simulated data. In all experiments, we initialize the mixture parameters for all methods using k-means++ [2]. |
| Researcher Affiliation | Academia | Reshad Hosseini School of ECE College of Engineering University of Tehran, Tehran, Iran EMAIL Suvrit Sra Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge, MA. EMAIL |
| Pseudocode | Yes | Algorithm 1: Sketch of an optimization algorithm (CG, LBFGS) to minimize f(X) on a manifold |
| Open Source Code | Yes | To aid reproducibility of our results, MATLAB implementations of our methods are available as a part of the MIXEST toolbox developed by our group [12]. |
| Open Datasets | Yes | Available at UCI machine learning dataset repository via https://archive.ics.uci.edu/ml/datasets |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions 'MATLAB implementations', 'MIXEST toolbox' [12], and 'MANOPT' [6], but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | In all experiments, we initialize the mixture parameters for all methods using k-means++ [2]. All methods also use the same termination criteria: they stop either when the difference of average log-likelihood (i.e., 1/n log-likelihood) between consecutive iterations falls below 10^-6, or when the number of iterations exceeds 1500. |