High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality
Authors: Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models. ... For a broad family of statistical models, our framework establishes the first computationally feasible approach for optimal estimation and asymptotic inference in high dimensions. |
| Researcher Affiliation | Academia | Zhaoran Wang Princeton University Quanquan Gu University of Virginia Yang Ning Princeton University Han Liu Princeton University |
| Pseudocode | Yes | Algorithm 1 High Dimensional EM Algorithm... Algorithm 2 Maximization Implementation of the M-step... Algorithm 3 Gradient Ascent Implementation of the M-step |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper discusses theoretical applications to 'Gaussian Mixture Model' and 'Mixture of Regression Model' but does not use or provide information for a specific training dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation or dataset splits. |
| Hardware Specification | No | The paper describes theoretical work and does not provide specific hardware details used for running experiments. |
| Software Dependencies | No | The paper describes theoretical work and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper describes theoretical work and does not provide specific experimental setup details. |