Sashi Novitasari, Takashi Fukuda, et al.
INTERSPEECH 2025
We model crowdsensing as the selection of sensors with unknown variance to monitor a large linear dynamical system. To achieve low estimation error, we propose a Thompson sampling approach combining submodular optimization and a scalable online variational inference algorithm to maintain the posterior distribution over the variance. We also consider three alternative parameter estimation algorithms. We illustrate the behavior of our sensor selection algorithms on real traffic data from the city of Dublin. Our online algorithm achieves significantly lower estimation error than sensor selection using a fixed variance value for all sensors.
Sashi Novitasari, Takashi Fukuda, et al.
INTERSPEECH 2025
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Paula Harder, Venkatesh Ramesh, et al.
EGU 2023
Fearghal O'Donncha, Albert Akhriev, et al.
Big Data 2021