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 [1].
k*-Nearest Neighbors: From Global to Local
Authors: Oren Anava, Kfir Levy
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 4 Experimental Results, The following experiments demonstrate the effectiveness of the proposed algorithm on several datasets. In our experiments we use 8 real-world datasets, Table 1: Experimental results. |
| Researcher Affiliation | Collaboration | Oren Anava The Voleon Group EMAIL, Kfir Y. Levy ETH Zurich EMAIL |
| Pseudocode | Yes | Algorithm 1 k-NN |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a repository for the described methodology. |
| Open Datasets | Yes | In our experiments we use 8 real-world datasets, all are available in the UCI repository website (https://archive.ics.uci.edu/ml/). |
| Dataset Splits | Yes | We randomly divide each dataset into two halves (one used for validation and the other for test). On the first half (the validation set), we run the two baselines and our algorithm with different values of k, σ and L/C (respectively), using 5-fold cross validation. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Specifically, we consider values of k in {1, 2, . . . , 10} and values of σ and L/C in {0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10}. |