Selective Inference for Sparse High-Order Interaction Models
Authors: Shinya Suzumura, Kazuya Nakagawa, Yuta Umezu, Koji Tsuda, Ichiro Takeuchi
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of the proposed methods through numerical experiments both on synthetic and real datasets. |
| Researcher Affiliation | Academia | 1Nagoya Institute of Technology, Nagoya, Japan 2University of Tokyo, Tokyo, Japan 3RIKEN, Tokyo, Japan. |
| Pseudocode | No | The paper describes algorithmic steps and theorems but does not include a clearly labeled pseudocode block or algorithm section. |
| Open Source Code | No | The paper does not provide any specific statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We applied the selective inference approach to HIV-1 sequence data obtained from Stanford HIV Drug Resistance Database (Rhee et al., 2003). |
| Dataset Splits | No | The paper discusses data splitting as a comparative baseline method ('split' approach) but does not provide specific train/validation/test dataset splits or percentages for the reproduction of its own model training and evaluation. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., CPU, GPU models, or memory specifications). |
| Software Dependencies | No | The paper does not mention specific software names with version numbers that would be necessary for reproduction. |
| Experiment Setup | Yes | We set the baseline parameters as n = 100, d = 100, k = 5, r = 5, α = 0.05, σ = 0.5, and ζ = 0.6. |