Bayesian Optimization of Partition Layouts for Mondrian Processes
Authors: Yi Wang, Bin Li, Xuhui Fan, Yang Wang, Fang Chen
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |