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..
Regularized EM Algorithms: A Unified Framework and Statistical Guarantees
Authors: Xinyang Yi, Constantine Caramanis
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Simulations We now provide some simulation results to back up our theory. We plot the log of errors over iteration t in Figure 1. In Figure 2, we plot bβ β 2 over normalized sample complexity... Each point is an average of 20 independent trials. |
| Researcher Affiliation | Academia | Xinyang Yi Dept. of Electrical and Computer Engineering The University of Texas at Austin EMAIL Constantine Caramanis Dept. of Electrical and Computer Engineering The University of Texas at Austin EMAIL |
| Pseudocode | Yes | Algorithm 1 Regularized EM Algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper describes generating synthetic data for simulations (e.g., 'X N(0, Ip), W N(0, σ2)') but does not provide concrete access information (link, DOI, repository, or formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper discusses splitting the dataset into T pieces for theoretical analysis (Algorithm 2) but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide 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) needed to replicate the experiment. |
| Experiment Setup | Yes | We use Algorithm 1 with T = 7, κ = 0.7, λ(0) n in Theorem 1. The choice of the critical parameter is given in the Supplementary Material. Settings: (a,b,d) (n, p, s) = (500, 800, 5); (d) (n, p, θ) = (600, 30, 3); (a-c) SNR = 5; (d) (SNR, ϵ) = (0.5, 0.2); (a-d) ω = 0.5. |