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..
Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search
Authors: Mohammad Ali Bashiri, Xinhua Zhang
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we compare the empirical performance of AFW-2 against related methods. We first illustrate the performance on kernel binary SVM, then we investigate a problem whose domain is not an SLP, and finally we demonstrate the scalability of AFW-2 on a large scale dataset. |
| Researcher Affiliation | Academia | Mohammad Ali Bashiri Xinhua Zhang Department of Computer Science, University of Illinois at Chicago Chicago, Illinois 60607 EMAIL |
| Pseudocode | Yes | Algorithm 1: Decomposition-invariant Away-step Frank-Wolfe (AFW) [...] Algorithm 2: Decomposition-invariant Pairwise Frank-Wolfe (PFW) (exactly the same as [18]) |
| Open Source Code | No | No explicit statement about providing open-source code or a direct link to a code repository for the described methodology was found. |
| Open Datasets | Yes | Three datasets are used. breast-cancer and a1a are obtained from the UCI repository [28] with n = 568 and 1, 605 training examples respectively, and ijcnn1 is from [29] with a subset of 5, 000 examples. [...] M. Lichman. UCI machine learning repository, 2013. URL http://archive.ics.uci.edu/ ml. |
| Dataset Splits | No | No explicit details about training/validation/test dataset splits (e.g., percentages, sample counts, or cross-validation setup) are provided. The paper mentions training examples and optimizing test accuracy, but not a distinct validation split. |
| Hardware Specification | No | No specific hardware details (e.g., CPU/GPU models, memory amounts, or detailed computer specifications) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names like PyTorch 1.9, or specific solver versions) were explicitly provided for the experimental setup. |
| Experiment Setup | Yes | We first run AFW-2 on the RC-Hull objective in (23), with the value of K set to optimize the test accuracy (K shown in Figure 1). [...] Figure 1: Comparison of SMO and AFW-2 on three different datasets (a) Breast-cancer (K = 10) (b) a1a (K = 30) (c) ijcnn1 (K = 20) [...] Figure 3: Full ijcnn1 (K = 100) |