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..
Blossom Tree Graphical Models
Authors: Zhe Liu, John Lafferty
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical properties and experiments with simulated and real data demonstrate the effectiveness of blossom trees. |
| Researcher Affiliation | Academia | Zhe Liu Department of Statistics University of Chicago; John Lafferty Department of Statistics Department of Computer Science University of Chicago |
| Pseudocode | No | The paper describes steps of the method but does not provide structured pseudocode or algorithm blocks with specific labels like "Algorithm" or "Pseudocode". |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We analyze a flow cytometry dataset on d = 11 proteins from [9]. |
| Dataset Splits | Yes | First, randomly partition the data X(1), . . . , X(n) into two sets D1 and D2 of sample size n1 and n2. Then apply the following steps. 1. Using D1, estimate the bivariate densities... 3. Using D2, choose b F (bk) b B from this family of blossom tree models that maximizes the heldout log-likelihood. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. |