Computational aspects of equilibria in discrete preference games
Phani Raj Lolakapuri, Umang Bhaskar, et al.
IJCAI 2019
Change point detection algorithms have numerous applications in areas of medical condition monitoring, fault detection in industrial processes, human activity analysis, climate change detection, and speech recognition. We consider the problem of change point detection on compositional multivariate data (each sample is a probability mass function), which is a practically important sub-class of general multivariate data. While the problem of change-point detection is well studied in univariate setting, and there are few viable implementations for a general multivariate data, the existing methods do not perform well on compositional data. In this paper, we propose a parametric approach for change point detection in compositional data. Moreover, using simple transformations on data, we extend our approach to handle any general multivariate data. Experimentally, we show that our method performs significantly better on compositional data and is competitive on general data compared to the available state of the art implementations.
Phani Raj Lolakapuri, Umang Bhaskar, et al.
IJCAI 2019
Krishnasuri Narayanam, Kameshwaran Sampath, et al.
ICBC 2021
Shubham Sahai, Nitin Singh, et al.
Blockchain 2020
Krishnasuri Narayanam, Akshar Kaul, et al.
BRAINS 2021