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..
The Power of Adaptivity for Stochastic Submodular Cover
Authors: Rohan Ghuge, Anupam Gupta, Viswanath Nagarajan
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real datasets show qualitative improvements in the solutions as we allow more rounds of adaptivity; in practice, solutions with a few rounds of adaptivity are nearly as good as fully adaptive solutions. |
| Researcher Affiliation | Academia | 1Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, USA. 2Department of Computer Science, Carnegie Mellon University, Pittsburgh, USA. |
| Pseudocode | Yes | Algorithm 1 PARtial Covering Algorithm PARCA(X, f, Q, δ) |
| Open Source Code | No | The paper does not provide any explicit statements about open-source code availability or links to code repositories for the described methodology. |
| Open Datasets | Yes | We use the Epinions network and a Facebook messages dataset described in (Rossi & Ahmed, 2015) to generate instances of the stochastic set cover problem. and We use a real-world dataset called WISER 2 for our experiments. ... This dataset has been used for evaluating algorithms for similar problems in other papers (Bhavnani et al., 2007; Bellala et al., 2011; Bellala et al., 2012; Navidi et al., 2020). |
| Dataset Splits | No | The paper describes sampling new realizations and running trials for evaluation, but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | Yes | We conducted all of our computational experiments using Python 3.8 and Gurobi 8.1 with a 2.3 Ghz Intel Core i5 processor and 16 GB 2133 MHz LPDDR3 memory. |
| Software Dependencies | Yes | We conducted all of our computational experiments using Python 3.8 and Gurobi 8.1 with a 2.3 Ghz Intel Core i5 processor and 16 GB 2133 MHz LPDDR3 memory. |
| Experiment Setup | Yes | We use δ = 0.5 for the Epinions network. However, since the Facebook messages network is sparse, we set δ = 0.25 in the second instance. and We generate costs randomly for each test from {1, 4, 7, 10} according to the weight vector [0.1, 0.2, 0.4, 0.3]; |