Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search
Authors: Mohammad Ali Bashiri, Xinhua Zhang
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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 {mbashi4,zhangx}@uic.edu |
| 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) |