Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Group Additive Structure Identification for Kernel Nonparametric Regression
Authors: Chao Pan, Michael Zhu
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,328 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,386 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 | Simulation study and real data applications demonstrate the effectiveness of the proposed method as a general tool for high dimensional nonparametric regression. |
| Researcher Affiliation | Academia | Pan Chao Department of Statistics Purdue University West Lafayette, IN 47906 EMAIL Michael Zhu Department of Statistics, Purdue University West Lafayette, IN 47906 Center for Statistical Science Department of Industrial Engineering Tsinghua University, Beijing, China EMAIL |
| Pseudocode | Yes | The two-step estimation is summarized in Algorithm 1. When a model contains a large number of predictor variables, such exhaustive search suffers high computational cost. In order to apply GASI on a large model, we propose a backward stepwise algorithm which is illustrated in Algorithm 2. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | In this section, we report the results of applying GASI on the Boston Housing data (another real data application is reported in the supplementary material). |
| Dataset Splits | Yes | Split data into training (T ) and validation (V) sets. the tuning parameters ยต and ฮฑ are selected via 10-fold CV. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | The grid values of ยต are equally spaced in [1e 10, 1/64] on a log-scale and each ฮฑ is an integer in [1, 10]. The noise ฯต is i.i.d. N(0, 0.012). |