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.
Label consistency in overfitted generalized $k$-means
Authors: Linfan Zhang, Arash Amini
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,835 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,873 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 | 3.3 Numerical experiments We ο¬rst 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 EMAIL Arash A. Amini Department of Statistics University of California, Los Angeles Los Angeles, CA 90095 EMAIL |
| 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 (Ο=0.1, Ο=1), and separation parameters (Ξ΄) 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. |