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..
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes
Authors: Jonathan Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that JUMP-means is competitive with or outperforms widely used MJP inference approaches in terms of both speed and reconstruction accuracy. |
| Researcher Affiliation | Academia | Jonathan H. Huggins* EMAIL Karthik Narasimhan* EMAIL Ardavan Saeedi* EMAIL Vikash K. Mansinghka EMAIL Computer Science and Artificial Intelligence Laboratory, MIT |
| Pseudocode | No | The paper describes algorithms in numbered step-by-step prose within sections titled 'Algorithm' (e.g., Section 3.3, 4.1), but does not present them in a structured pseudocode block format. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | For our experiments, we use a real-world dataset collected from a phase III clinical trial of a drug for MS. (Mandel, 2010). We use data from the MIMIC database (Goldberger et al., 2000; Moody & Mark, 1996) |
| Dataset Splits | No | The paper mentions holding out data for testing but does not explicitly specify a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions implementation languages like 'Java' and 'Python' but does not provide specific version numbers for them or any other software dependencies. |
| Experiment Setup | Yes | We set the hyperparameters ξ, ξλ, and µλ equal to 1, 1, and .5, respectively. (Section 5.1); The hyperparameters γ and ξ1 are set to 5, while ζ, ξ, and ξ2 are set to 0.005. (Section 5.2) |