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..
A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models
Authors: En-Hsu Yen, Xin Lin, Kai Zhong, Pradeep Ravikumar, Inderjit Dhillon
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments on several benchmark data sets, the proposed method finds optimal solution of the combinatorial problem and significantly improves existing methods in terms of the exemplar-based objective. |
| Researcher Affiliation | Academia | 1 Department of Computer Science, University of Texas at Austin, TX 78712, USA. 2 Institute for Computational Engineering and Sciences, University of Texas at Austin, TX 78712, USA. 3 Department of Statistics and Data Sciences, University of Texas at Austin, TX 78712, USA. |
| Pseudocode | Yes | Algorithm 1 ADMM for exemplar-based HDP mixture (6) |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | Our experiments for DP mixture model are conducted on 5 publicly available data sets: Iris, Glass, Wine, DNA and Segment. For HDP mixture model, we experiment on Wholesale and Water data sets. ... All of the data sets can be downloaded from UCI Machine Learning Repository |
| Dataset Splits | No | The paper mentions the use of datasets for experiments but does not provide specific details on training, validation, or test splits, nor does it describe cross-validation setups. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not specify versions for any software dependencies or libraries used in the implementation or experiments. |
| Experiment Setup | Yes | Since the algorithm gives different result for different order of updating {z(t) i }N i=1, we run the algorithm for 1000 rounds with random permutation on the updating in order to achieve better local optimum. Global mean is used as initialization as specified in (Kulis & Jordan, 2012). ... In our experiment, we fix maximum number of Frank-Wolfe iterations to 30. |