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..
Submodular Cost Submodular Cover with an Approximate Oracle
Authors: Victoria Crawford, Alan Kuhnle, My Thai
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compute the approximation ratios stated in Theorems 1 and 2 on instances of the Influence Threshold problem (IT), a special case of SCSC. We use the nonsubmodular approximate reachability oracle that has been proposed by Cohen et al. (2014). |
| Researcher Affiliation | Academia | 1Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, United States 2Department of Computer Science, Florida State University, Tallahassee, Florida, United States. |
| Pseudocode | Yes | Algorithm 1 greedy(F, c, τ) Input:A value oracle to F : 2S R 0, a value oracle to c : 2S R 0, and τ. Fτ = min{F, τ} A = while F(A) < τ do u = argmaxx S\A Fτ(A, x)/c(x) A = A {u} end while return A |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | We use two real social networks: the Facebook ego network (Leskovec & Mcauley, 2012), and the Ar Xi V General Relativity collaboration network (Leskovec et al., 2007), which we refer to as Gr Qc. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper has an "Experimental Setup" section but it lacks specific hyperparameter values, detailed training configurations, or system-level settings, often deferring them to an appendix not provided in the main text. |