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 [1].
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling
Authors: Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan Suykens4844-4851
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several benchmark datasets demonstrate that our algorithm achieves comparable prediction performance and takes less time cost when compared to (Li et al. 2019). |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering (ESAT-STADIUS), KU Leuven, Belgium 2Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China 3Institute of Medical Robotics, Shanghai Jiao Tong University, China 4School of Operations Research and Information Engineering, Cornell University, USA |
| Pseudocode | Yes | Algorithm 1: The Surrogate Leverage Weighted RFF Algorithm in KRR |
| Open Source Code | Yes | The source code of our implementation can be found in http://www.lfhsgre.org. |
| Open Datasets | Yes | These datasets can be downloaded from https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/ datasets/ or the UCI Machine Learning Repository2. |
| Dataset Splits | Yes | The regularization parameter λ is tuned via 5-fold inner cross validation over a grid of {0.05, 0.1, 0.5, 1}. |
| Hardware Specification | Yes | All experiments are implemented in MATLAB and carried out on a PC with Intel i5-6500 CPU (3.20 GHz) and 16 GB RAM. |
| Software Dependencies | No | The paper states 'All experiments are implemented in MATLAB', but it does not specify a version number for MATLAB or any other software libraries used, which is required for reproducibility. |
| Experiment Setup | Yes | We choose the popular shift-invariant Gaussian/RBF kernel for experimental validation, i.e., k(x, x ) = exp( x x 2/σ2). Following (Avron et al. 2017), we use a fixed bandwidth σ = 1 in our experiments. The regularization parameter λ is tuned via 5-fold inner cross validation over a grid of {0.05, 0.1, 0.5, 1}. |