Structure Learning of Partitioned Markov Networks
Authors: Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The performance of the proposed method is experimentally compared with the state of the art MN structure learning methods using ROC curves. Real applications on analyzing bipartisanship in US congress and pairwise DNA/time-series alignments are also reported. |
| Researcher Affiliation | Academia | The Institute of Statistical Mathematics, Tokyo 190-8562, Japan. Tokyo Institute of Technology, Tokyo 152-8552, Japan; PRESTO, Japan Science and Technological Agency (JST). University of Tokyo, Chiba 277-8561, Japan. |
| Pseudocode | No | No structured pseudocode or algorithm block was found. |
| Open Source Code | No | The paper does not provide a specific repository link or an explicit statement about the public availability of its source code. |
| Open Datasets | Yes | We use the proposed method to study the bipartisanship between Democrats and Republicans in the 109th US Senate via the recorded votes. There were totally 100 senators (45 Democrats and 55 Republicans) casting votes on 645 questions with yea , nay or not voting . The task is to discover the cross-party links between senators. |
| Dataset Splits | No | λ is a regularization parameter that can be tuned via cross-validation. However, no specific dataset split percentages or counts for training/validation/test were provided. |
| Hardware Specification | No | No specific hardware details (like GPU models, CPU types, or cloud resources) used for running the experiments were provided. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | Unless specified otherwise, we use pairwise feature function ψ(xu, xv) = xuxv. No other specific setup details, such as learning rates, batch sizes, or optimizers, are provided. |