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].
Row-clustering of a Point Process-valued Matrix
Authors: Lihao Yin, Ganggang Xu, Huiyan Sang, Yongtao Guan
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of the proposed framework is demonstrated through simulation studies and real data analyses. |
| Researcher Affiliation | Academia | Institute of Statistics and Big Data, Renmin University, Beijing, China, 2Department of Statistics, Texas A&M University, College Station, Texas, 3University of Miami, Coral Gables, Florida |
| Pseudocode | Yes | Algorithm 1 Learning of the Single-level model in (2) |
| Open Source Code | Yes | Our code can be accessed via https://github.com/Lihao Yin/MMMPP. |
| Open Datasets | Yes | The City of Chicago collected the information of all taxi rides in Chicago since 2013 1. Each trip record in the dataset consists of drivers encrypted IDs, pick-up/drop-off time stamps, and locations (in the form of latitude/longitude coordinates). Footnote 1: https://data.cityofchicago.org/Transportation/Taxi-Trips/wrvz-psew |
| Dataset Splits | Yes | We evaluate and compare clustering stability based on a measure called clustering consistency via K-trial cross-validations [23, 24]... We compare the performance of DF, DMHP, and MM-MPP in terms of clustering consistency for three data sets with K = 100 trials. |
| Hardware Specification | Yes | Table 2: Running Time (in seconds) on Synthetic Data lists: GPU-ES (RTX 8000 48G GPU), CPU-ES (i7-7700HQ CPU), CPU-EM (i7-7700HQ CPU). |
| Software Dependencies | No | The paper mentions software like the 'rtweet' API [12] and 'NumPy' [7] but does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | Section 5.1 'Setting' states: 'We set the number of clusters C from 2 to 5 and set the number of accounts in each cluster to 500. We experiment with an increasing number of replicates (m = 1, 20 or 100)'. Additionally, it notes: 'For each iteration, 10,000 MCMC samples are drawn to approximate (10)'. |