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..
Sparse Polynomial Learning and Graph Sketching
Authors: Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G Dimakis, Adam Klivans
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also provide experimental results on a real world dataset. We test our algorithms on a real dataset and show that the algorithm is able to scale well on sparse hypergraphs created out of Yahoo! messenger dataset by filtering through time and location stamps. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, 2Department of Computer Science The University of Texas at Austin, USA EMAIL, EMAIL EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Learn Bool |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We run our algorithm on the Yahoo! Messenger User Communication Pattern Dataset [17]. [17] Yahoo, Yahoo! webscope dataset ydata-ymessenger-user-communication-pattern-v1 0, http: //research.yahoo.com/Academic Relations. |
| Dataset Splits | No | The paper describes parameters for data collection and interval selection (e.g., δt, δx, m) but does not provide specific train/validation/test dataset split percentages or counts, or refer to predefined splits. |
| Hardware Specification | Yes | We performed simulations using MATLAB on an Intel(R) Xeon(R) quad-core 3.6 GHz machine with 16 GB RAM and 10M cache. |
| Software Dependencies | No | The paper mentions using 'MATLAB' but does not specify its version number or any other software dependencies with specific version details. |
| Experiment Setup | Yes | For each time interval, the error probability is calculated by averaging the number of errors among 100 different trials. Table 1: Simulation parameters for Fig. 1b |