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..
Bayesian Optimization of Partition Layouts for Mondrian Processes
Authors: Yi Wang, Bin Li, Xuhui Fan, Yang Wang, Fang Chen
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical tests demonstrate that Bayesian optimization is able to find better partition structures than MCMC sampling with the same number of partition structure proposals. |
| Researcher Affiliation | Collaboration | Data61, CSIRO, Eveleigh NSW 2015, Australia School of CSE, The University of New South Wales, Kensington NSW 2033, Australia |
| Pseudocode | Yes | Algorithm 1 Bayesian Optimization for the MP Relational Model |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that its source code is open or publicly available. |
| Open Datasets | Yes | We adopt six real-world relational data sets, including 3 directed graphs and 3 undirected graphs, for testing. ... The adopted three preprocessed data sets Epinions200 (E200), Slashdot200 (S200) and Wikivote200 (W200) are from [Leskovec and Krevl, 2014]3. ... The above three data sets have been extensively used for link prediction [Hoff, 2008; Miller et al., 2009; Lloyd et al., 2012]. |
| Dataset Splits | No | The paper mentions using a 'subset of each data set by selecting the top 200 users' and details iteration counts for burn-in and prediction, but it does not specify explicit percentages or sample counts for training, validation, or test data splits. |
| 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 provide specific names or version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | We use the same budget λ = 2 for all the compared methods in all the experiments. For the hyper-parameters of the block intensity, we set α0 = 1 and β0 = 1. The cutting rate matrix for the initialization step in GPUCB-MP and EI-MP are randomly generated by κk,l ∼ Uniform(0, 1). ... For RJMCMC-MP, 400 outer iterations (accepted structure change proposals) are conducted; while for GPUCB-MP and EI-MP, 100 outer iterations are conducted for initialization and 300 outer iterations are conducted for prediction. |