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..
Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs
Authors: Stephen Bach, Bert Huang, Jordan Boyd-Graber, Lise Getoor
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate on social-group detection, trust prediction in social networks, and image reconstruction, finding that paired-dual learning trains models as accurate as those trained by traditional methods in much less time, often before traditional methods make even a single parameter update. |
| Researcher Affiliation | Academia | Stephen H. Bach University of Maryland, College Park, MD Bert Huang Virginia Tech, Blacksburg, VA Jordan Boyd-Graber University of Colorado, Boulder, CO Lise Getoor University of California, Santa Cruz, CA |
| Pseudocode | Yes | Algorithm 1 Paired-Dual Learning |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use the data of Bach et al. (2013a)...We evaluate on a subsample of roughly 2,000 users of Epinions.com (Huang et al., 2013; Richardson et al., 2003)...Using the 400-image Olivetti face data set... |
| Dataset Splits | Yes | measuring the area under the precision recall curve (Au PR) using ten folds of cross-validation...perform eight-fold cross-validation |
| Hardware Specification | No | The paper mentions 'avoiding confounding factors such as differences in hardware used in these experiments' but does not provide specific details on the hardware (e.g., GPU/CPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies or libraries used in the experiments (e.g., Python, PyTorch, specific solvers). |
| Experiment Setup | Yes | We test two variants of paired-dual learning: the finest grained interleaving with only two ADMM iterations per weight update (N = 1) and a coarser grained 20 ADMM iterations per update (N = 10)...In our experiments (Section 4), K = 0 often suffices, but for one task, using K = 10 produces a better start to optimization. |