Permuton-induced Chinese Restaurant Process
Authors: Masahiro Nakano, Yasuhiro Fujiwara, Akisato Kimura, Takeshi Yamada, naonori ueda
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that PCRP can improve the prediction performance in relational data analysis by reducing the local optima and slow mixing problems compared with the conventional BNP models because the local transitions of PCRP in Markov chain Monte Carlo inference are more flexible than the previous models. |
| Researcher Affiliation | Industry | Masahiro Nakano, Yasuhiro Fujiwara, Akisato Kimura, Takeshi Yamada, Naonori Ueda NTT Communication Science Laboratories, NTT Corporation |
| Pseudocode | No | The paper describes the model steps and inference algorithm in text, but it does not include a formally structured pseudocode block or an algorithm figure. |
| Open Source Code | Yes | Our code will be available at https://github.com/nttcslab/permuton-induced-crp. |
| Open Datasets | Yes | Datasets Four social network datasets [40]: Wiki [1], Facebook [2], Twitter [3], and Epinions [4]. All data is public and does not contain any personally identifiable information (See [41] for license). |
| Dataset Splits | Yes | We held out 20% cells of the input data for testing, and each model was trained by the MCMC using the remaining 80% of the cells. |
| Hardware Specification | No | The main paper does not explicitly describe the hardware used. It refers to the supplementary material for 'the total amount of compute and the type of resources used'. |
| Software Dependencies | No | The paper does not explicitly provide specific software dependencies with version numbers in the main text. It refers to the supplementary material for experimental settings. |
| Experiment Setup | No | The paper states that 'The detailed experimental setup is described in the supplementary material.', but does not include specific hyperparameter values or training configurations in the main text. |