Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Train and Test Tightness of LP Relaxations in Structured Prediction
Authors: Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David Sontag
ICML 2016 | Venue PDF | LLM Run Details | Input Tokens: 17,696 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,029 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL Mehrdad Mahdavi EMAIL Toyota Technological Institute at Chicago Adrian Weller EMAIL University of Cambridge David Sontag EMAIL 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. |