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..
Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models
Authors: Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct a number of experiments on synthetic and real data to evaluate the quality of models learned by both Naive MLE and CGM. |
| Researcher Affiliation | Academia | 1University of Massachusetts Amherst 2Mount Holyoke College 3Colgate University. Correspondence to: Garrett Bernstein <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Non-Linear Belief Propagation (NLBP) ... Algorithm 2 EM for CGMs |
| Open Source Code | No | No statement regarding the release or availability of open-source code for the described methodology was found. |
| Open Datasets | No | The paper uses synthetic data and human mobility data, but provides no concrete access information (links, DOIs, repository names, or citations with author/year) for either dataset. |
| Dataset Splits | No | The paper mentions reserving 25% of individuals' data for testing but does not specify training/validation/test splits with exact percentages, sample counts, or a clear splitting methodology for reproduction. |
| Hardware Specification | No | No specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) are provided. |
| Experiment Setup | No | The paper mentions data preprocessing steps and that PSGD was tuned via grid search, but it does not provide specific hyperparameter values, training configurations, or system-level settings for the models. |