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..
Continuous DR-submodular Maximization: Structure and Algorithms
Authors: An Bian, Kfir Levy, Andreas Krause, Joachim M. Buhmann
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical findings are validated on synthetic and real-world problem instances. |
| Researcher Affiliation | Academia | An Bian ETH Zurich EMAIL Kfir Y. Levy ETH Zurich EMAIL Andreas Krause ETH Zurich EMAIL Joachim M. Buhmann ETH Zurich EMAIL |
| Pseudocode | Yes | Algorithm 1: TWO-PHASE FRANK-WOLFE for non-monotone DR-submodular maximization |
| Open Source Code | Yes | All experiments were implemented using MATLAB. Source code can be found at: https://github.com/bianan/non-monotone-dr-submodular. |
| Open Datasets | Yes | In our experiments, we used a similar setting to the one in [20]. We experimented on the 2012 US Republican debates data, which consists of 8 candidates: Bachman, Gingrich, Huntsman, Paul, Perry, Romney and Santorum. |
| Dataset Splits | No | The paper mentions using synthetic and real-world data but does not provide explicit details on train/validation/test splits, such as percentages, sample counts, or references to predefined splits. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, memory, or cloud instance types) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper states 'All experiments were implemented using MATLAB.' and refers to 'QUADPROGIP2 [39]' and 'IBM CPLEX optimization studio' as subroutines. However, specific version numbers for MATLAB or CPLEX are not provided, preventing full reproducibility of the software environment. |
| Experiment Setup | Yes | We run all the algorithms for 100 iterations. For the subroutine (Algorithm 3) of TWO-PHASE FRANK-WOLFE, we set 1 = 2 = 10 6, K1 = K2 = 100. ... In order to make f non-monotone, we set h = 0.2 H> u. To make sure that f is non-negative, we first of all solve the problem minx2P 1 2x>Hx + h>x using QUADPROGIP, let the solution to be ˆx, then set c = f(ˆx) + 0.1 |f(ˆx)|. |