Optimal Convergence Rates for Distributed Nystroem Approximation
Authors: Jian Li, Yong Liu, Weiping Wang
JMLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct experiments on several real-world datasets to validate the effectiveness of the proposed algorithm, and the empirical results coincide with our theoretical findings. In this section, we study the generalization performance on real-world datasets. We implement all methods based on Pytorch 1.13 1 and run experiments on a Linux Server with an Nvidia RTX 2080Ti GPU. |
| Researcher Affiliation | Academia | Jian Li EMAIL Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China Yong Liu EMAIL Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China Weiping Wang EMAIL Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China |
| Pseudocode | Yes | Algorithm 1 Distributed Nystr om approximation (DNystr om) |
| Open Source Code | Yes | 1. Publicly available at https://github.com/superlj666/DNystroem |
| Open Datasets | Yes | We evaluate the compared algorithms on real-world classification datasets, which are publicly available from UCI datasets 2. Available at http://archive.ics.uci.edu/ml/datasets.php |
| Dataset Splits | Yes | Table 1: The statistics and tuned hyperparameters in datasets usps 10 7291 2007 256 10 10 6 pendigits 10 7494 3498 16 100 10 6 letter 26 15000 5000 16 1 10 7 MNIST 10 60000 10000 784 10 10 6 |
| Hardware Specification | Yes | We implement all methods based on Pytorch 1.13 1 and run experiments on a Linux Server with an Nvidia RTX 2080Ti GPU. |
| Software Dependencies | Yes | We implement all methods based on Pytorch 1.13 1 and run experiments on a Linux Server with an Nvidia RTX 2080Ti GPU. |
| Experiment Setup | Yes | Using the toolbox NNI 3, we tune the optimal hyperparameters over the grids σ {10i, i = 4, 3, , 4} and λ {10i, i = 10, , 1}. The statistics information and optimal hyperparameters for datasets are recorded in Table 1. |