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..
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality
Authors: Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu
NeurIPS 2015 | Venue PDF | 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. |