Risk-Sensitive Submodular Optimization
Authors: Bryan Wilder
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results in two sensor placement domains confirm that our algorithm substantially outperforms competitive baselines. |
| Researcher Affiliation | Academia | Bryan Wilder Department of Computer Science and Center for Artificial Intelligence in Society University of Southern California bwilder@usc.edu |
| Pseudocode | Yes | Algorithm 1 RASCAL |
| Open Source Code | No | The paper does not include a statement about releasing the source code or a link to a code repository for its methodology. |
| Open Datasets | Yes | First, netscience2: a collaboration network of network science researchers with 1461 nodes. Second, euroroad: a network of European cities and roads between them, with 1,174 nodes. Third, synthetic Watts-Strogatz networks (parameters k = 2, p = 0.1). ... Second, we consider detecting contamination in a water network via the Battle of Water Sensor Networks (BWSN). BWSM (Ostfeld et al. 2008) simulates the spread of contamination through a 126-node water network...The network is a real water distribution network from an anonymous location, and the t values are provided by EPANET, a highly realistic water distribution simulator designed by the U.S. Environmental Protection Agency. http://www-personal.umich.edu/ mejn/netdata/ |
| Dataset Splits | No | The paper mentions simulating scenarios but does not provide specific details on train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments, only mentioning that the algorithm 'scales easily to 10,000 nodes, running in under 1 minute'. |
| Software Dependencies | No | The paper mentions the use of EPANET, but does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | For all networks, λ = 5, p = 0.01, and we simulate 1000 scenarios (random source nodes and propagation times). |