Sparse Polynomial Learning and Graph Sketching
Authors: Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G Dimakis, Adam Klivans
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | 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 mkocaoglu@utexas.edu, karthiksh@utexas.edu dimakis@austin.utexas.edu, klivans@cs.utexas.edu |
| 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 |