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..
Exact Inference for Integer Latent-Variable Models
Authors: Kevin Winner, Debora Sujono, Dan Sheldon
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our techniques are more scalable than existing approximate methods and enable new applications. |
| Researcher Affiliation | Academia | 1College of Information and Computer Sciences, University of Massachusetts Amherst 2Department of Computer Science, Mount Holyoke College. |
| Pseudocode | Yes | Algorithm 1 Ak(sk) and Algorithm 2 LAk(hsk, dskiq) GDUAL-FORWARD are provided in the paper. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the methodology described is publicly available. |
| Open Datasets | No | The paper uses simulated data for its experiments but does not provide concrete access information (link, DOI, repository, or formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper uses simulated data and discusses experimental results, but it does not specify explicit train/validation/test dataset splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names). |
| Experiment Setup | Yes | The paper describes aspects of the experimental setup for parameter estimation, including using the 'L-BFGS-B algorithm', '10 random restarts', generating '10 independent observation vectors for K = 7 time steps', and repeating experiments '50 times'. |