Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-Objective Submodular Maximization by Regret Ratio Minimization with Theoretical Guarantee
Authors: Chao Feng, Chao Qian12302-12310
AAAI 2021 | Venue PDF | 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. |