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..
Probabilistic Partial Canonical Correlation Analysis
Authors: Yusuke Mukuta, Harada
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical experiments demonstrated that our methods can stably estimate the model parameters, even in high dimensions or when there are a small number of samples. |
| Researcher Affiliation | Academia | Yusuke Mukuta EMAIL Graduate School of Information Science and Technology, The University of Tokyo 7 3 1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan Tatsuya Harada EMAIL Graduate School of Information Science and Technology, The University of Tokyo 7 3 1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan |
| Pseudocode | No | The paper describes methods through mathematical formulations and text but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | Next, we applied GSPCCA and PCCA to meteorological data, using the Global Summary of the Day (GSOD) provided by the National Climatic Data Center (NCDC) on its website. |
| Dataset Splits | No | The paper mentions 'five-fold cross validation (CV)' as a model selection technique, but does not explicitly provide the training, validation, or test dataset splits needed to reproduce the main experiments. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or specialized solvers. |
| Experiment Setup | Yes | In our experiments, we set a0, b0 = 10 14, νm 0 = dm, Km 0 = 10 14 Idm. The ARD prior drives unnecessary components to zero, so we can estimate the dimensions of the latent variables by choosing sufficiently large dz, or by first choosing a small dz and then gradually increasing it according to the output projection matrices. We refer to this model as Bayesian PCCA (BPCCA). |