Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs
Authors: Stephen Bach, Bert Huang, Jordan Boyd-Graber, Lise Getoor
ICML 2015 | Conference PDF | Archive PDF | Plain Text | 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. |