Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Manifold-valued Dirichlet Processes
Authors: Hyunwoo Kim, Jia Xu, Baba Vemuri, Vikas Singh
ICML 2015 | Venue PDF | LLM Run Details | Input Tokens: 18,650 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,745 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |