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].
Understanding Doubly Stochastic Clustering
Authors: Tianjiao Ding, Derek Lim, Rene Vidal, Benjamin D Haeffele
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct a variety of experiments that illustrate our theoretical findings and demonstrate the utility of doubly stochastic projection in different settings. |
| Researcher Affiliation | Academia | 1Mathematical Institute for Data Science, Johns Hopkins University, USA 2Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. There is no specific repository link, explicit code release statement, or mention of code in supplementary materials. |
| Open Datasets | No | The paper describes how data was generated (e.g., 'We generate two subspaces of dimension d in RD=20', 'We use a WSBM with 5 blocks and 50 points per block') rather than using or providing access to a publicly available dataset with a specific link, DOI, repository, or formal citation. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning into train/validation/test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We run the DS-D(K, η) studied by this paper on the K with varying η {0.002, 0.004, 0.01}... The parameter γ in LSR is set to be 10. |