Multi-Objective Submodular Maximization by Regret Ratio Minimization with Theoretical Guarantee
Authors: Chao Feng, Chao Qian12302-12310
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on the applications of multi-objective weighted maximum coverage and Max-Cut show the superior performance of RRMS over POLYTOPE. |
| Researcher Affiliation | Academia | Chao Feng,1,2 Chao Qian1 1 State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2 School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China |
| Pseudocode | Yes | Algorithm 1 RRMS Algorithm and Algorithm 2 Weight Vector Sampling Procedure are explicitly presented with numbered steps. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We use the real-world data set email-Eu-core from http: //snap.stanford.edu/data/#email; We use the real-world data set American College football from http://www-personal.umich.edu/ mejn/netdata/ |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. The problem involves subset selection from a given dataset, not a traditional machine learning model training/evaluation setup with explicit data splits. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used to run the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions using 'the greedy algorithm' and 'the randomized algorithm using semidefinite programming (Goemans 1995)' as approximation algorithms, but does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | The number d of objectives is set from 2 to 7. For each d, the number k of selected solutions is set from 2d to 26 with an interval of 2. The budget b is set to 100. we repeat the running ten times independently and report the mean and standard deviation of the estimated regret ratio. the parameter α = 0.87856. |