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..
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs
Authors: Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet Dokania, Simon Lacoste-Julien
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide an exhaustive empirical evaluation of all our methods on four structured prediction datasets. |
| Researcher Affiliation | Academia | 1INRIA Ecole Normale Sup erieure, Paris, France 2INRIA Centrale Sup elec, Chˆatenay-Malabry, France |
| Pseudocode | Yes | Algorithm 1 Block-Coordinate Frank-Wolfe (BCFW) algorithm for structured SVM |
| Open Source Code | Yes | The code and datasets are available at our project webpage.2 2 http://www.di.ens.fr/sierra/research/gap BCFW/ |
| Open Datasets | Yes | We evaluate our methods on four datasets for different structured prediction tasks: OCR (Taskar et al., 2003) for handwritten character recognition, Co NLL (Tjong Kim Sang & Buchholz, 2000) for text chunking, Horse Seg (Kolesnikov et al., 2014) for binary image segmentation and LSP (Johnson & Everingham, 2010) for pose estimation. |
| Dataset Splits | No | The paper mentions using a 'test performance' value for λ, but does not provide specific train/validation/test split percentages or sample counts for any of the datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU/GPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We report the results of each method on 6 datasets (including 3 sizes of Horse Seg) for three values of the regularization parameter λ: the value leading to the best test performance, a smaller and a larger value. For each setup, we report the duality gap against both number of oracle calls and elapsed time. We run each method 5 times with different random seeds influencing the order of sampled objects and report the median (bold line), minimum and maximum values (shaded region). |