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..
Benefits of over-parameterization with EM
Authors: Ji Xu, Daniel J. Hsu, Arian Maleki
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The goal of this article is to present theoretical and empirical evidence that over-parameterization can help EM avoid spurious local optima in the log-likelihood. ... For other Gaussian mixtures, we provide empirical evidence that shows similar behavior. ... In this section, we present numerical results that show the value of over-parameterization in some mixture models not covered by our theoretical results. |
| Researcher Affiliation | Academia | Ji Xu Columbia University EMAIL Daniel Hsu Columbia University EMAIL Arian Maleki Columbia University EMAIL |
| Pseudocode | No | The paper describes algorithms using mathematical equations (e.g., equations 3, 4, 5, 6, 7) but does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block with structured steps. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement, or code in supplementary materials) for the source code of the described methodology. |
| Open Datasets | No | The paper uses synthetic data generated from Gaussian mixture models as described in equation (2) (e.g., 'y1, ..., yn comprise an i.i.d. sample from a mixture of k Gaussians'). No specific, publicly available, or open dataset with access information (link, DOI, formal citation) is mentioned. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) 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 like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | For each case, we run EM with 2500 random initializations and compute the empirical probability of success. When n = 1000, the initial mean parameter is chosen uniformly at random from the sample. When n = ∞, the initial mean parameter is chosen uniformly at random from the rectangle [-2, +2]x[-2, +2]. Specific configurations for 'separation' and 'mixing weight' are given (e.g., 'separation |µ2-µ1| ∈ {1, 2, 4}', 'mixing weight w1 ∈ {0.52, 0.7, 0.9}'). |