Primal-Dual Block Generalized Frank-Wolfe
Authors: Qi Lei, JIACHENG ZHUO, Constantine Caramanis, Inderjit S. Dhillon, Alexandros G. Dimakis
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show that our algorithm outperforms the state-of-the-art methods on (multi-class) classification tasks. |
| Researcher Affiliation | Collaboration | UT Austin Amazon {leiqi@oden., jzhuo@, constantine@, inderjit@cs., dimakis@austin.}utexas.edu |
| Pseudocode | Yes | Algorithm 1 Primal-Dual Block Generalized Frank-Wolfe Method for ℓ1 Norm Ball |
| Open Source Code | Yes | The codes to reproduce our results could be found in https://github.com/Carlson Zhuo/ primal_dual_frank_wolfe. |
| Open Datasets | Yes | The six datasets used here are summarized in Table 2. All of them can be found in LIBSVM datasets [4]. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | Yes | Algorithms are implemented in C++, with the Eigen linear algebra library [12]. |
| Experiment Setup | Yes | We set the ℓ1 constraint to be 300 and the ℓ2 regularize parameter to 10/n to achieve reasonable prediction accuracy. |