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.
Consistency of the $k_n$-nearest neighbor rule under adaptive sampling
Authors: Robi Bhattacharjee, Geelon So, Sanjoy Dasgupta
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 29,577 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,356 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the kn-nearest neighbor learner within this setting. In the worst-case, the learner will fail because an adaptive process can generate spurious patterns out of noise. However, under the mild smoothing assumption that the process generating the instances is uniformly absolutely continuous and that choice of (kn)n is reasonable, the kn-nearest neighbor rule is online consistent. |
| Researcher Affiliation | Academia | Robi Bhattacharjee1 Sanjoy Dasgupta2 Geelon So2 1University of Tübingen and Tübingen AI Center 2Department of Computer Science and Engineering, UC San Diego |
| Pseudocode | Yes | Algorithm 1 The kn-nearest neighbor rule 1: for n = 1, 2, . . . do 2: Receive the instance Xn 3: Predict the majority vote label of the kn nearest neighbors X(1) n , . . . , X(kn) n , k=1 Y (k) n 1/2 4: Observe and memorize the label Yn 5: end for |
| Open Source Code | No | No experiments. (from checklist) |
| Open Datasets | No | No experiments. (from checklist) |
| Dataset Splits | No | No experiments. (from checklist) |
| Hardware Specification | No | No experiments. (from checklist) |
| Software Dependencies | No | No experiments. (from checklist) |
| Experiment Setup | No | No experiments. (from checklist) |