Effective Distributed Learning with Random Features: Improved Bounds and Algorithms
Authors: Yong Liu, Jiankun Liu, Shuqiang Wang
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we validate our theoretical findings by performing experiments on both simulated and real datasets. Numerical Experiments. Inspired by numerical experiments in (Rudi & Rosasco, 2017; Li et al., 2019e), we consider a spline kernel of order q... and Real Data. In this experiment, we consider the performance on real data. We use 6 publicly available datasets from LIBSVM Data. |
| Researcher Affiliation | Academia | Yong Liu1,2, Jiankun Liu3, Shuqiang Wang4 1Gaoling School of Artificial Intelligence, Renmin University of China 2Beijing Key Laboratory of Big Data Management and Analysis Methods 3Institute of Information Engineering, Chinese Academy of Sciences 4Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1 Distributed KRR with Random Features and Communications (DKRR-RF-CM) |
| Open Source Code | No | The paper does not provide a statement about releasing its own source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We use 6 publicly available datasets from LIBSVM Data2. and Footnote 2: http://www.csie.ntu.edu.tw/ cjlin/libsvm. |
| Dataset Splits | Yes | We generate 10000 samples for training and 10000 samples for testing. and ...and fine tune λ around |D| 1/2 using 5-fold cross validation1... |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set the size of the random features to be M = p|D|, and fine tune λ around |D| 1/2 using 5-fold cross validation1, the tuned set is {2 5, 2 3, . . . 25}|D| 1/2. and The empirical evaluations with Gaussian kernel, exp( x x 2/σ), are given in Figure 2, where the optimal σ and λ are selected by 5-fold cross-validation, σ {2i, i = 10, 8, . . . , 10}, {2 5, 2 3, . . . 25}|D| 1/2, and the number of random features is 2 p|D|. |