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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Scaling-Up Split-Merge MCMC with Locality Sensitive Sampling (LSS)
Authors: Chen Luo, Anshumali Shrivastava4464-4471
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Overall, we obtain a superior tradeoff between convergence and per update cost. As a direct consequence, our proposals are around 6X faster than the state-of-the-art sampling methods on two large real datasets KDDCUP and Pub Med with several millions of entities and thousands of clusters. |
| Researcher Affiliation | Academia | Chen Luo, Anshumali Shrivastava Department of Computer Science, Rice University EMAIL |
| Pseudocode | No | The paper describes the proposed algorithms textually and with mathematical equations but does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a direct link to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluate the effectiveness of our algorithm on both two large real-world datasets: KDDCUP and Pub Med. KDDCUP data was used in the KDD Cup 2004 data mining competition. [...] 1https://cs.joensuu.fi/sipu/datasets/ The Pub Med abstraction dataset [...] 2www.pubmed.gov |
| Dataset Splits | No | The paper mentions using KDDCUP, Pub Med, and synthetic datasets but does not explicitly state the proportions or methodology for train/validation/test splits for any of them. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU/GPU models, memory specifications). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | No | The paper mentions parameters like K and L for LSH methods and k for synthetic data generation, but it does not specify general experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or optimizer settings. |