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 [1].
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood
Authors: Kohei Hayashi, Shin-ichi Maeda, Ryohei Fujimaki
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A demonstrative study on Bayesian principal component analysis is provided and numerical experiments support our theoretical results. |
| Researcher Affiliation | Collaboration | Kohei Hayashi EMAIL Global Research Center for Big Data Mathematics, National Institute of Informatics Kawarabayashi Large Graph Project, ERATO, JST Shin-ichi Maeda EMAIL Graduate School of Informatics, Kyoto University Ryohei Fujimaki EMAIL NEC Knowledge Discovery Research Laboratories |
| Pseudocode | Yes | Algorithm 1 The g FAB algorithm |
| Open Source Code | No | The paper does not provide any statements about the availability of open-source code or links to repositories. |
| Open Datasets | No | The paper uses synthetic data: 'We used the synthetic data X = ZW + E where W uniform([0, 1])4, Z N(0, I), and End N(0, σ2). Under the data dimensionality D = 30 and the true model K = 10, we generated data with N = 100, 500, 1000, and 2000.' No concrete access information for a publicly available dataset is provided. |
| Dataset Splits | No | The paper describes generating synthetic data for different sample sizes (N), but does not specify any training, validation, or test dataset splits or cross-validation setup. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processors, or memory used for the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | We stopped the algorithms if the relative error was less than 10 5 or the number of iterations was greater than 104. |