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..
Stochastic Expectation Maximization with Variance Reduction
Authors: Jianfei Chen, Jun Zhu, Yee Whye Teh, Tong Zhang
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare s EM-vr with batch EM, s EM and other algorithms on Gaussian mixture models and probabilistic latent semantic analysis, and s EM-vr converges significantly faster than these baselines. |
| Researcher Affiliation | Collaboration | Dept. of Comp. Sci. & Tech., BNRist Center, State Key Lab for Intell. Tech. & Sys., Institute for AI, THBI Lab, Tsinghua University, Beijing, 100084, China Department of Statistics, University of Oxford Tencent AI Lab |
| Pseudocode | Yes | We have pseudocode for s EM and s EM-vr in Appendix D. |
| Open Source Code | No | The paper does not provide concrete access to source code, such as a specific repository link or an explicit statement about code release for the methodology described. |
| Open Datasets | Yes | We compare s EM-vr with b EM and s EM (SCVB0), which is the start-of-the-art algorithm for p LSA, on four datasets listed in Table 1. ... NIPS [1] ... NYTimes [1] ... Wiki [38] ... Pub Med [1]. [1] Arthur Asuncion and David Newman. Uci machine learning repository, 2007. |
| Dataset Splits | No | The paper mentions assessing convergence on the training objective and holding out a testing set, but it does not provide specific details about training, validation, and test dataset splits (e.g., percentages or sample counts for each split). |
| Hardware Specification | Yes | The testing machine has two 12-core Xeon E5-2692v2 CPUs and 64GB main memory. |
| Software Dependencies | No | The paper states that 'All the algorithms are implemented in C++', but does not provide specific version numbers for the C++ compiler or any other software libraries or dependencies used. |
| Experiment Setup | Yes | For each dataset and the number of topics K {50, 100}, we first select the hyperparameters by a grid search Kα {0.1, 1, 10, 100} and β {0.01, 0.1, 1}.3 Then, we do another grid search to choose the step size. For s EM-vr, we choose ρ {0.01, 0.02, 0.05, 0.1, 0.2}, and for all other stochastic algorithms, we set ρt = a/(t + t0)κ, and choose a {10 7, . . . , 100}, t0 {10, 100, 1000} and κ {0.5, 0.75, 1}.4 Finally, we repeat 5 runs with difference random seeds for each algorithm with its best step size. E is 20 for NIPS and NYTimes, and 5 for Wiki and Pub Med. M is 50 for NIPS and 500 for all the other datasets. |