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.
Modeling Correlated Arrival Events with Latent Semi-Markov Processes
Authors: Wenzhao Lian, Vinayak Rao, Brian Eriksson, Lawrence Carin
ICML 2014 | Venue PDF | LLM Run Details | Input Tokens: 16,980 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,547 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 | apply our ideas to both synthetic data and a real-world biometrics application. We evaluate the performance of our model and inference methodology on both synthetic and real-world biometrics data. |
| Researcher Affiliation | Collaboration | Wenzhao Lian EMAIL Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA Vinayak Rao EMAIL Department of Statistical Science, Duke University, Durham, NC 27708, USA Brian Eriksson EMAIL Technicolor Research Center, 735 Emerson Street, Palo Alto, CA 94301, USA Lawrence Carin EMAIL Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA |
| Pseudocode | Yes | Algorithm 1 in the appendix gives details of this generative process. |
| Open Source Code | Yes | Code available at http://people.duke.edu/~wl89/ |
| Open Datasets | No | The paper uses synthetic data and a custom-collected biometrics dataset but does not provide concrete access information or state its public availability. |
| Dataset Splits | No | The paper describes how the synthetic data was generated and the MCMC iterations (e.g., 'discarding the first 2000 as burn-in'), but does not specify explicit training, validation, and test splits for the real-world dataset. |
| Hardware Specification | Yes | The running time of a typical trial (with T = 1000 and about 120 event arrivals for each user) was about 3000 seconds with unoptimized Matlab code on a computer with 2.2GHz CPU and 8GB RAM. |
| Software Dependencies | No | The paper mentions 'unoptimized Matlab code' but does not specify the version number of Matlab or any other software dependencies with their versions. |
| Experiment Setup | Yes | For inference, the fixed hyperparameters of the sampler were set as: α = 3, c = d = e = f = 10 3, and πk = [0.5, 0.5]T . We ran 5000 MCMC iterations of our MCMC sampler, discarding the first 2000 as burn-in, with posterior samples collected every 5 iterations. |