Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Provable Variable Selection for Streaming Features
Authors: Jing Wang, Jie Shen, Ping Li
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 16,453 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,105 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |