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.
Fast Computation of Leave-One-Out Cross-Validation for $k$-NN Regression
Authors: Motonobu Kanagawa
TMLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 9,714 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,747 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 | Numerical experiments confirm the validity of the fast computation method. We empirically check the validity of the formula (7) for efficient LOOCV computation. We consider a real-valued regression problem where X = Rd and Y = R, using two real datasets from scikit-learn: Diabetes and Wine . |
| Researcher Affiliation | Academia | Motonobu Kanagawa EMAIL Data Science Department EURECOM |
| Pseudocode | No | The paper describes the method using mathematical formulas (Lemma 1, Corollary 1) and natural language, without structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for reproducing the experiments is available on https://github.com/motonobuk/LOOCV-kNN |
| Open Datasets | Yes | We consider a real-valued regression problem where X = Rd and Y = R, using two real datasets from scikit-learn: Diabetes and Wine . |
| Dataset Splits | Yes | LOOCV for k-NN regression is defined as follows. ... For each ℓ= 1, . . . , n, consider the training dataset (1) with the ℓ-th pair (xℓ, yℓ) removed: Dn\{(xℓ, yℓ)} = {(x1, y1), . . . , (xℓ 1, yℓ 1), (xℓ+1, yℓ+1), . . . , (xn, yn)}. |
| Hardware Specification | Yes | CPU: 1.1 GHz Quad-Core Intel Core i5. Memory: 8 GB 3733 MHz LPDDR4X. |
| Software Dependencies | No | The paper mentions using 'scikit-learn' for implementing k-NN regression but does not specify a version number for scikit-learn or any other software dependency. Footnote 2 points to a general stable documentation URL rather than a specific version. |
| Experiment Setup | Yes | We standardized each input feature to have mean zero and unit variance. ... We show the LOOCV scores computed by the two methods for different values of k... for fixed k = 5. |