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.
Memory Efficient Kernel Approximation
Authors: Si Si, Cho-Jui Hsieh, Inderjit Dhillon
ICML 2014 | Venue PDF | LLM Run Details | Input Tokens: 17,369 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,071 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 | In this section, we empirically demonstrate the benefits of our proposed method, MEKA on various data sets. |
| Researcher Affiliation | Academia | Si Si EMAIL Cho-Jui Hsieh EMAIL Inderjit S. Dhillon EMAIL Department of Computer Science, The University of Texas, Austin, TX 78721, USA |
| Pseudocode | Yes | Algorithm 1: Memory Efficient Kernel Approximation (MEKA) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | All the datasets are downloaded from www.csie.ntu. edu.tw/ cjlin/libsvmtools/datasets and UCI repository(Bache & Lichman, 2013). |
| Dataset Splits | Yes | The parameters are chosen by five fold cross-validation and shown in Table 3. |
| Hardware Specification | Yes | All the experiments are conducted on a machine with an Intel Xeon X5440 2.83GHz CPU and 32G RAM. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names). |
| Experiment Setup | Yes | The parameters are chosen by five fold cross-validation and shown in Table 3. |