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.
Discrete Rényi Classifiers
Authors: Meisam Razaviyayn, Farzan Farnia, David Tse
NeurIPS 2015 | Venue PDF | LLM Run Details | Input Tokens: 14,906 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,912 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 | Finally, we numerically compare our proposed algorithm with the DCC classifier and show that the proposed algorithm results in better misclassification rate over various UCI data repository datasets. |
| Researcher Affiliation | Academia | Department of Electrical Engineering, Stanford University, Stanford, CA 94305. |
| Pseudocode | Yes | Algorithm 1 Robust R enyi Feature Selection |
| Open Source Code | No | The paper does not provide any explicit statement about releasing its source code or a link to a code repository for its methodology. |
| Open Datasets | Yes | We evaluated the performance of the R enyi classifiers eδ and eδ map on five different binary classification datasets from the UCI machine learning data repository. |
| Dataset Splits | No | The results are averaged over 100 Monte Carlo runs each using 70% of the data for training and the rest for testing. |
| Hardware Specification | No | The paper discusses training times but does not specify the exact hardware (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Matlab SVM command' but does not provide specific version numbers for Matlab or any other software dependencies. |
| Experiment Setup | Yes | The value of λridge and λ is determined through cross validation. |