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.
A Minimax Approach to Supervised Learning
Authors: Farzan Farnia, David Tse
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 14,630 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,724 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 | We perform several numerical experiments to show the power of the minimax SVM in outperforming the SVM. |
| Researcher Affiliation | Academia | Farzan Farnia EMAIL David Tse EMAIL Department of Electrical Engineering, Stanford University, Stanford, CA 94305. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluated the performance of the minimax SVM on six binary classification datasets from the UCI repository |
| Dataset Splits | No | We determined the parameters by cross validation, where we used a randomly-selected 70% of the training set for training and the rest 30% for testing. ... We performed this procedure in 1000 Monte Carlo runs each training on 70% of the data points and testing on the rest 30% and averaged the results. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Matlab s SVM command' but does not provide specific version numbers for Matlab or any other software dependencies. |
| Experiment Setup | Yes | We implemented the minimax SVM by applying the subgradient descent to (18) with the regularizer λ α 2 2. We determined the parameters by cross validation, where we used a randomly-selected 70% of the training set for training and the rest 30% for testing. We tested the values in {2 10, . . . , 210}. |