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.
Kernel Feature Selection via Conditional Covariance Minimization
Authors: Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I. Jordan
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,210 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,815 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 prove various consistency results for this procedure, and also demonstrate that our method compares favorably with other state-of-the-art algorithms on a variety of synthetic and real data sets. |
| Researcher Affiliation | Academia | Jianbo Chen University of California, Berkeley EMAIL Mitchell Stern University of California, Berkeley EMAIL Martin J. Wainwright University of California, Berkeley EMAIL Michael I. Jordan University of California, Berkeley EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for our approach is publicly available at https://github.com/Jianbo-Lab/CCM. |
| Open Datasets | Yes | We carry out experiments on 12 standard benchmark tasks from the ASU feature selection website [17] and the UCI repository [18]. |
| Dataset Splits | Yes | Performance is then measured by training a kernel SVM on the top m features and computing the resulting accuracy as measured by 5-fold cross-validation. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, or cloud computing specifications) are provided for running experiments. |
| Software Dependencies | No | The paper mentions using "Scikit-learn [20] and Scikit-feature [17] packages" and the "author’s implementation for BAHSIC2" but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | For kernel-based methods, we use a Gaussian kernel k(x, x) = exp( kx xk2/(2σ2)) on X and a linear kernel k(y, y) = y T y on Y . We take σ to be the median pairwise distance between samples scaled by 1/√2. We use " = 0.001 for the classification tasks and " = 0.1 for the regression task, selecting these values from {0.001, 0.01, 0.1} using cross-validation. For our own algorithm, we fix " = 0.001 across all experiments and set the number of desired features to m = 100 if d > 100 or dd/5e otherwise. In all cases we fix the regularization constant of the SVM to C = 1. |