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..
Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data
Authors: Yubo Zhuang, Xiaohui Chen, Yun Yang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numeric experiments demonstrate that i LASDP can achieve lower mis-clustering errors over several widely used clustering methods including K-means, SDP and EM algorithms. In this section, we compare the performance of several widely used clustering methods on two real datasets. |
| Researcher Affiliation | Academia | 1Department of Statistics, University of Illinois at Urbana Champaign. 2Department of Mathematics, University of Southern California. Correspondence to: Yubo Zhuang <EMAIL>, Xiaohui Chen <EMAIL>, Yun Yang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 The iterative likelihood adjusted SDP (i LASDP) algorithm |
| Open Source Code | No | The paper does not contain any explicit statement or link providing access to the source code for the methodology described. |
| Open Datasets | Yes | The first one is handwriting digits dataset MNIST, the second one is CIFAR-10 image dataset and the last one is Landsat dataset from the UCI machine learning repository. |
| Dataset Splits | No | The paper mentions the use of a "test set" for various datasets (MNIST, CIFAR-10, Landsat) but does not provide explicit details about training or validation splits (e.g., percentages or sample counts for all splits), only the size of the test sets. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions "hierarchical clustering from mclust package in R" but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | For all the dimension reduction procedures used in the simulation experiments, we perform step 1-7 in Algorithm 2 followed by Algorithm 3 with input parameters α = 0.7, C = 1010, p0 = 2K, p1 = 15 ϵ = 10 2, S = 50. |