Submodular Optimization over Streams with Inhomogeneous Decays
Authors: Junzhou Zhao, Shuo Shang, Pinghui Wang, John C.S. Lui, Xiangliang Zhang5861-5868
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on real data demonstrate that HISTSTREAMING can find high quality solutions and is up to two orders of magnitude faster than the naive GREEDY algorithm. |
| Researcher Affiliation | Collaboration | 1King Abdullah University of Science and Technology, KSA 2Inception Institute of Artificial Intelligence, UAE 3Xi an Jiaotong University, China 4The Chinese University of Hong Kong, Hong Kong |
| Pseudocode | Yes | Algorithm 1: BASICSTREAMING; Algorithm 2: HISTAPPROX; Algorithm 3: HISTSTREAMING |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code. |
| Open Datasets | Yes | DBLP. We construct a representative author selection problem on the DBLP dataset (DBLP 2018), which records the meta information of about 3 million papers, including 1.8 million authors and 5,079 conferences from 1936 to 2018. Stack Exchange. We construct a hot question selection problem on the math.stackexchange.com website (Stack Exchange 2018). The dataset records about 1.3 million questions with 152 thousand commenters from 7/2010 to 6/2018. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We run the three proposed algorithms for 100 time steps and maintain a set with cardinality k = 10 at every time step. We set ϵ = 0.1. |