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].
Fuzzy Clustering with Similarity Queries
Authors: Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we test our algorithms over real-world datasets, showing their effectiveness in real-world applications. |
| Researcher Affiliation | Academia | Wasim Huleihel Department of Electrical Engineering Tel Aviv University Tel Aviv 6997801, Israel EMAIL Arya Mazumdar Halıcıo glu Data Science Institute University of California, San Diego La Jolla, CA 92093 EMAIL Soumyabrata Pal College of Information & Computer Sciences University of Massachusetts Amherst Amherst, MA 01003 EMAIL |
| Pseudocode | Yes | Due to space limitation, proofs, some pseudocodes, experimental results over both synthetic and real-world datasets, and conclusions are relegated to the supplementary material. Algorithm 1 Parallel algorithm for approximating P. Algorithm 2 MEMBERSHIP(X, bµj, , ). Algorithm 3 BINARYSEARCH(X, , x). Algorithm 4 Sequential algorithm for approximating P. Algorithm 5 Sequential algorithm for approximating P with two clusters |
| Open Source Code | No | The paper mentions pseudocodes and experimental results are in supplementary material, but does not provide a concrete link or explicit statement about releasing source code for the methodology. |
| Open Datasets | No | The paper mentions using "real-world datasets" and "synthetic datasets" and refers to their use in experiments. However, it does not provide concrete access information (link, DOI, specific repository, or detailed citation) for any publicly available dataset within the main text. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset split percentages, sample counts, or explicit methodology for data partitioning needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, 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 like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |