Provable Variable Selection for Streaming Features
Authors: Jing Wang, Jie Shen, Ping Li
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical results on realworld data sets demonstrate the effectiveness of our algorithm. |
| Researcher Affiliation | Collaboration | 1Cornell University, New York, NY 10021, USA. 2Rutgers University, Piscataway, NJ 08854, USA. 3Baidu Research, Bellevue, WA 98004, USA. |
| Pseudocode | Yes | Algorithm 1 Online Feature Selection |
| Open Source Code | No | The paper does not provide a specific repository link or explicitly state that the code for the methodology described is released. |
| Open Datasets | Yes | Data Sets. We perform the experiments on 6 realistic data sets, including USPS1, AR2, COIL203, CIFAR-104, MNIST5 and ORL6. The summary of them is shown in Table 2. 1https://archive.ics.uci.edu/ml/datasets. html |
| Dataset Splits | No | The paper references datasets but does not explicitly provide specific training/validation/test dataset splits, percentages, or counts, nor does it cite a predefined split methodology for these datasets. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'standard k-means clustering that is available in Matlab' but does not specify version numbers for Matlab or any other software dependencies needed to reproduce the experiments. |
| Experiment Setup | No | The paper mentions parameters like 'sampling rate c = 8ϵ 2 log n' and 'approximation parameter ϵ (0, 1)' and that 'k-means clustering is available in Matlab', but it does not provide specific hyperparameter values or detailed training configurations (e.g., number of clusters k for k-means, learning rates, epochs, specific optimization settings) used in the experiments. |