Train and Test Tightness of LP Relaxations in Structured Prediction
Authors: Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David Sontag
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present some numerical results to support our theoretical analysis. We run experiments for a multi-label classification task and an image segmentation task. |
| Researcher Affiliation | Collaboration | Ofer Meshi MESHI@TTIC.EDU Mehrdad Mahdavi MAHDAVI@TTIC.EDU Toyota Technological Institute at Chicago Adrian Weller AW665@CAM.AC.UK University of Cambridge David Sontag DSONTAG@CS.NYU.EDU New York University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code for the described methodology. |
| Open Datasets | Yes | Finally, we conduct experiments on a foreground-background segmentation problem using the Weizmann Horse dataset (Borenstein et al., 2004). |
| Dataset Splits | Yes | For training we have implemented the block-coordinate Frank-Wolfe algorithm for structured SVM (Lacoste-Julien et al., 2013), using GLPK as the LP solver. We use a standard L2 regularizer, chosen via cross-validation. |
| Hardware Specification | No | The paper does not specify any hardware details (CPU, GPU models, memory, or specific computing environments) used for running experiments. |
| Software Dependencies | No | The paper mentions 'GLPK as the LP solver' but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | No | The paper mentions 'standard L2 regularizer' and 'using GLPK as the LP solver' but does not provide specific hyperparameter values (e.g., learning rate, batch size) or other concrete system-level training settings. |