Theoretical guarantees for EM under misspecified Gaussian mixture models
Authors: Raaz Dwivedi, nhật Hồ, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical findings in different cases via several numerical examples. |
| Researcher Affiliation | Collaboration | Raaz Dwivedi Nhat Ho Koulik Khamaru UC Berkeley {raaz.rsk, minhnhat, koulik}@berkeley.edu Martin J. Wainwright UC Berkeley Voleon Group wainwrig@berkeley.edu Michael I. Jordan UC Berkeley jordan@berkeley.edu |
| Pseudocode | No | The paper describes the EM algorithm mathematically but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | No | The paper uses simulated data for its numerical examples rather than a publicly available dataset. It states, 'data is generated according to some true distribution P'. |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits; it describes numerical examples with simulated data rather than empirical experiments on partitioned datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments or simulations. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers used for the numerical examples. |
| Experiment Setup | Yes | Specific parameters for the numerical examples are given, such as 'Case 1: θ = 5, ρ = 0.2', 'Case 2: θ = 5, ω = 0.2', 'Case 3: θ = 0.5', and 'starting point θ0 B(θ, θ /4)'. |