Online Non-Monotone DR-Submodular Maximization

Authors: Nguyễn Kim Thắng, Abhinav Srivastav9868-9876

AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Finally we run experiments to verify the performance of our algorithms on problems arising in machine learning domain with the real-world datasets.
Researcher Affiliation Academia Nguyễn Kim Thắng, Abhinav Srivastav IBISC, Univ. Evry, University Paris-Saclay, France kimthang.nguyen@univ-evry.fr, abhinavsriva@gmail.com
Pseudocode Yes Algorithm 1 Online algorithm for vee learning
Open Source Code No The paper does not provide any explicit statement about releasing its source code or a link to a code repository for the methodology described.
Open Datasets No The paper mentions using "the Facebook dataset" but does not provide specific access information such as a link, DOI, repository name, or a formal citation with authors and year to confirm its public availability.
Dataset Splits No The paper does not explicitly provide details about training, validation, or test dataset splits; it describes how batches are constructed for the online setting.
Hardware Specification No The paper mentions that experiments were performed on "MAC OS version 10.15" but does not provide specific hardware details such as exact CPU or GPU models, or memory specifications.
Software Dependencies No The paper states that experiments were performed using "MATLAB" and "CPLEX optimization tool" but does not provide specific version numbers for these software components, which are necessary for reproducibility.
Experiment Setup Yes We choose the number of time steps to be T = 1000. At each time t 1, . . . , T, we randomly uniformly select 2000 vertices V t V , independently of V 1, . . . , V t 1, and construct a batch Bt with edge-weights wt ij = 1 if and only if i, j V t and edge (i, j) exists in the Facebook dataset. In case if i or j do not belong to V t, wt ij = 0. We set p = 0.0001.