Label consistency in overfitted generalized $k$-means
Authors: Linfan Zhang, Arash Amini
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3.3 Numerical experiments We first consider the (noiseless) line-circle model in R3, an example of mixture-of-curves. |
| Researcher Affiliation | Academia | Linfan Zhang Department of Statistics University of California, Los Angeles Los Angeles, CA 90095 linfanz@g.ucla.edu Arash A. Amini Department of Statistics University of California, Los Angeles Los Angeles, CA 90095 aaamini@ucla.edu |
| Pseudocode | No | The paper provides mathematical formulations and proofs but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See the supplemental material. |
| Open Datasets | No | The paper uses synthetic models (line-circle model, line-Gaussian model, circle-torus model) defined within the paper, which are not externally available datasets with concrete access information (e.g., URL, DOI, or specific citation to an established public dataset repository). |
| Dataset Splits | No | The paper states the total number of data points sampled for numerical experiments (e.g., n = 3000) but does not specify any train, validation, or test splits. |
| Hardware Specification | No | The paper states "Computing details are in the supplemental material" in its checklist, but the main body of the paper does not specify any particular hardware (e.g., GPU/CPU models, memory, or cluster specifications) used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for any libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | No | The paper describes simulation parameters like sample size (n=3000), noise levels (σ=0.1, σ=1), and separation parameters (δ) for its numerical experiments. However, it explicitly states "We did not do data training" in its checklist, implying there are no machine learning-specific hyperparameters or system-level training settings. |