Information Gathering With Peers: Submodular Optimization With Peer-Prediction Constraints
Authors: Goran Radanovic, Adish Singla, Andreas Krause, Boi Faltings
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate our methods using a realistic crowd sensing testbed. ... We perform two different tests. In the first test, we vary the total available budget B from 5 to 25 at steps of size 5. At the same time, we keep the minimal expected payment to τmin = 0.5. As we can see from Figure 2a, as the total budget increases, all the methods perform better. ... The results are given in Figure 2b. |
| Researcher Affiliation | Academia | Goran Radanovic Harvard University Cambridge, USA gradanovic@g.harvard.edu Adish Singla MPI-SWS Saarbr ucken, Germany adishs@mpi-sws.org Andreas Krause ETH Zurich Zurich, Switzerland krausea@ethz.ch Boi Faltings EPFL Lausanne, Switzerland boi.faltings@epfl.ch |
| Pseudocode | Yes | Algorithm 1: Algorithm PPCGREEDY... Algorithm 2: Algorithm PPCGREEDYITER |
| Open Source Code | No | The paper does not include any explicit statements or links indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We use a crowd sensing test-bed of Singla (2017), constructed from real measurements of CO2 and user locations across an urban area. ... obtained using a publicly available data (Open Street Map5), from which we randomly selected 300 locations from an area in the center of New York City. ... inferred from a publicly accessible dataset (Strava6) that contains the mobility patterns of cyclists for a period of 6 days. |
| Dataset Splits | No | The paper describes how the dataset was collected and sampled but does not specify a training, validation, or test split for the data used in experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as CPU/GPU models or cloud instance types. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages or libraries. |
| Experiment Setup | Yes | In the first test, we vary the total available budget B from 5 to 25 at steps of size 5. At the same time, we keep the minimal expected payment to τmin = 0.5. ... In the second test, we vary the minimal expected payment τmin, which now takes values in {0.1, 0.25, 0.5, 0.75, 0.9}. Budget B is set to 15. |