Streaming Algorithm for Monotone k-Submodular Maximization with Cardinality Constraints
Authors: Alina Ene, Huy Nguyen
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we experimentally evaluate our algorithm for k-submodular maximization (Algorithm 1). Following previous work (Ohsaka & Yoshida, 2015; Nguyen & Thai, 2020), we evaluate the algorithms on instances of influence maximization with k topics and sensor placement with k measurements. We follow the experimental setup of these prior works. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Boston University 2Khoury College of Computer and Information Science, Northeastern University. Correspondence to: Alina Ene <aene@bu.edu>, Huy Nguyen <hu.nguyen@northeastern.edu>. |
| Pseudocode | Yes | Algorithm 1 Algorithm for monotone k-submodular maximization. Parameters: C, D ... Algorithm 2 Algorithm for monotone submodular maximization with a partition matroid constraint. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | We used the Facebook dataset from the SNAP database (Leskovec & Krevl, 2014). ... We used the Intel Lab dataset (Bodik et al., 2004) which contains approximately 2.3 million readings from 58 sensors deployed in the Intel Berkeley research lab. |
| Dataset Splits | No | The paper uses the Facebook and Intel Lab datasets but does not explicitly provide details about training, validation, or test splits, nor does it reference predefined splits with specific citations or splitting methodology. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | In all experiments, we set the parameters C and D of Algorithm 1 as follows: D = B 21/B 1 , where B = mini [k] Bi is the minimum budget, and C = 0.5D. |