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..
Thinning for Accelerating the Learning of Point Processes
Authors: Tianbo Li, Yiping Ke
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic and real-world datasets validate the effectiveness of thinning in the tasks of parameter and gradient estimation, as well as stochastic optimization. |
| Researcher Affiliation | Academia | Tianbo Li, Yiping Ke School of Computer Science and Engineering Nanyang Technological University, Singapore EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: TSGD: Thinning Stochastic Gradient Descent |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | IPTV dataset [24]: The dataset consists of IPTV viewing events... NYC Taxi dataset: The data is from The New York City Taxi and Limousine Commission1... Weeplace dataset [23]: This dataset contains the check-in histories of users at different locations. |
| Dataset Splits | No | The paper mentions training and test datasets, but does not explicitly provide details on a validation set or cross-validation strategy. |
| Hardware Specification | Yes | All the experiments were conducted on a server with Intel Xeon CPU E5-2680 (2.80GHz) and 250GB RAM. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We ran each method on each dataset for 10 times. For each dataset, we perform LSE with different histories: full data and p-thinned data with p = 0.2 and p = 0.5. |