Blossom Tree Graphical Models
Authors: Zhe Liu, John Lafferty
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | 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. |