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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Big Learning Expectation Maximization
Authors: Yulai Cong, Sijia Li
AAAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 20,038 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,329 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through simulated experiments, we empirically show that the Big Learn-EM is capable of delivering the optimal with high probability; comparisons on benchmark clustering datasets further demonstrate its effectiveness and advantages over existing techniques. |
| Researcher Affiliation | Academia | Yulai Cong*, Sijia Li Sun Yat-sen University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Big Learning Expectation Maximization |
| Open Source Code | Yes | The code is available at https://github.com/Yulai Cong/Big-Learning Expectation-Maximization. |
| Open Datasets | Yes | To validate the effectiveness of the Big Learn-EM in real-world clustering applications, we conduct comprehensive experiments on diverse clustering datasets, including Connect-4, Covtype, Glass, Letter, Pendigits, Satimage, Seismic, Svmguide2, and Vehicle (see Appendix B for details). We follow the experimental setup in Cai et al. (2022) and conduct an experiment on the Fashion MNIST dataset. |
| Dataset Splits | No | The paper describes the use of 'test joint KL divergence' but does not specify the explicit percentages or sample counts for training, validation, and test splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'scipy.stats.ortho group' in Algorithm 1, implying Python and SciPy usage, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Algorithm 1: Input: Training data, the number K of mixture components, probabilities [P1, P2] for joint and marginal matchings, and the number W of local updates. The training objective in (12) also mentions a hyper-parameter γ and η > 0 is a small constant (11). |