Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate
Authors: Akifumi Okuno, Hidetoshi Shimodaira
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We theoretically prove that the MS-k-NN attains the improved rate, which coincides with the existing optimal rate under some conditions. Numerical experiments are conducted for performing MS-k-NN. |
| Researcher Affiliation | Collaboration | Akifumi Okuno1,3 and Hidetoshi Shimodaira2,3 1School of Statistical Thinking, The Institute of Statistical Mathematics 2Graduate School of Informatics, Kyoto University 3RIKEN Center for Advanced Intelligence Project |
| Pseudocode | No | The paper describes a minimization problem formally: 'ˆb := arg min b RC+1 ˆη(k NN) n,kv (X ) b0 c=1 bcr2c v' but this is a mathematical expression, not a pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We employ datasets from UCI Machine Learning Repository (Dua & Graff, 2017). |
| Dataset Splits | Yes | Feature vectors are first normalized, and then randomly divided into 70% for prediction (npred = 0.7n ) and the remaining for test query. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments (e.g., CPU, GPU models, or cloud computing instances). |
| Software Dependencies | No | The paper does not mention specific software names with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | Parameter tuning: For unweighted and weighted k-NN, we first fix k := V n4/(4+d) pred n4/(4+d) pred . Using the same k, we simply choose k1 := k/V, k2 = 2k/V, . . . , k V = k with V = 5 for MS-k-NN. Regression in MS-k-NN is ridge regularized with the coefficient λ = 10 4. |