Streaming k-Submodular Maximization under Noise subject to Size Constraint
Authors: Lan Nguyen, My T. Thai
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally investigate our algorithms performance in comparison with existing methods on two applications of Noisy Mk SC: Influence Maximization with k topics and Sensor Placement with k measures. The experimental results show our algorithms perform comparatively to state-of-the-art non-streaming algorithms in quality of solutions but outperform them in term of number of queries. |
| Researcher Affiliation | Academia | Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, United States. Correspondence to: Lan N. Nguyen <lan.nguyen@ufl.edu>, My T. Thai <mythai@cise.ufl.edu>. |
| Pseudocode | Yes | Algorithm 1 DSTREAM with known f(o)Input F, B, k and o that f(o) o B f(o)/(1 + γ); Algorithm 2 DSTREAMInput V, F, k, B, M > 1, γ > 0; Algorithm 3 RSTREAMInput F, k, B, M > 1, γ > 0, η 1 |
| Open Source Code | Yes | 1The source code is available at https://github.com/ lannn2410/streamingksubmodular |
| Open Datasets | Yes | We use Facebook dataset from SNAP database (Leskovec & Krevl, 2014), an undirected graph with 4,039 nodes and 88,234 edges. |
| Dataset Splits | No | The paper mentions using 'Facebook dataset' and 'Intel Lab dataset' but does not provide specific details on how these datasets were split into training, validation, or test sets, nor does it refer to predefined splits with citations. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as CPU models, GPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions models and algorithms like 'Linear Threshold (LT) Model' and 'SSA algorithm' used in the experiments, but it does not specify any software dependencies (e.g., programming languages, libraries, or frameworks) with version numbers. |
| Experiment Setup | Yes | We set k = 3, ϵ = 0.5 and compare our algorithms with the following: Greedy... Streaming Greedy (SGr)... To be specific, we vary M between 3, 4, 5 and γ between 0.5, 1.0, 1.5. Elements in V are observed in random order. We compare algorithms performance in various values of B. Results are averaged over 3 repetitions. |