Regularized EM Algorithms: A Unified Framework and Statistical Guarantees
Authors: Xinyang Yi, Constantine Caramanis
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | 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 yixy@utexas.edu Constantine Caramanis Dept. of Electrical and Computer Engineering The University of Texas at Austin constantine@utexas.edu |
| 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. |