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..
Non-Monotone Adaptive Submodular Maximization
Authors: Alkis Gotovos, Amin Karbasi, Andreas Krause
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have evaluated our proposed algorithm on the two objective functions described in the previous section, namely influence maximization and maximum cut, on a few real-world data sets. |
| Researcher Affiliation | Academia | Alkis Gotovos ETH Zurich Amin Karbasi Yale University Andreas Krause ETH Zurich |
| Pseudocode | Yes | Algorithm 1 Adaptive random greedy |
| Open Source Code | No | No explicit statement or link regarding the public release of source code was found. |
| Open Datasets | Yes | For our experiments, we used networks from the KONECT2 database, which accumulates network data sets from various other sources. [...] [Mc Auley and Leskovec, 2012]. |
| Dataset Splits | No | The paper does not provide explicit details about train/validation/test dataset splits for model training in a traditional sense. It mentions subsampling networks and evaluating on 'random realizations' and 'random ground sets' but no percentages or counts for distinct training, validation, and testing partitions. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers were listed (e.g., Python 3.x, TensorFlow x.x). |
| Experiment Setup | Yes | For the influence maximization objective, the influence propagation probability of each edge is chosen to be p = 0.1, and for the maximum cut objective, selecting a node cuts that node or one of its neighbors with equal probability. [...] we subsample each network down to 2000 nodes, [...] select uniformly at random a subset of 100 nodes as the ground set E, and repeat the experiments for 50 such random ground sets. |