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..
Manifold-valued Dirichlet Processes
Authors: Hyunwoo Kim, Jia Xu, Baba Vemuri, Vikas Singh
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments To evaluate the proposed model, we conduct a set of experiments on synthetic and real-world data. |
| Researcher Affiliation | Academia | University of Wisconsin-Madison, Madison, WI 53706, USA University of Florida, Gainesville, FL 32611, USA |
| Pseudocode | Yes | Algorithm 1 HMC algorithm for DP-MGLM on Riemannian manifolds |
| Open Source Code | No | The paper provides a project page URL (http://pages.cs.wisc.edu/~hwkim/projects/dp-mglm/) but does not explicitly state that source code is provided there, nor is it a direct link to a code repository like GitHub. |
| Open Datasets | Yes | We used the Lifespan database (Minear & Park, 2004), which contains 580 subjects with ages ranging from 18 93. |
| Dataset Splits | No | The paper mentions 'Train' and 'Test' in Table 1 and sample sizes ('Our sample size is 300') but does not specify the explicit percentages or counts for training, validation, and testing splits. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments (e.g., specific CPU or GPU models, or cloud computing environments). |
| Software Dependencies | No | The paper does not mention any specific software or library names with version numbers (e.g., Python, PyTorch, TensorFlow, etc.) that were used. |
| Experiment Setup | Yes | We perform a few hundred realizations where the number of MCMC samples in each realization is 1000. We set the burn-in period to 100 epochs. |