Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Online Non-Monotone DR-Submodular Maximization
Authors: Nguyễn Kim Thắng, Abhinav Srivastav9868-9876
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,720 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,878 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL, EMAIL |
| 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. |