Direct queries and paired comparisons are popular means of data acquisition in various scientific disciplines. These two types of data have been studied separately, however, several modern applications -- particularly those involving human judgments -- require analysis of a combination of these two types of data. Such an analysis presents novel opportunities and challenges for stochastic modeling, experimental design and algorithm development. This research involves establishing a unified view of learning from direct measurements and paired comparisons with the aim of understanding fundamental limits and tradeoffs for various problems of interest, and developing and implementing practical algorithms that can leverage both types of data. The utility of the algorithms will be demonstrated by application to diverse domains including material science and crowdsourcing.
This project focuses on the specific technical problems of function estimation, feature selection, and optimization that can leverage passively and actively acquired direct queries and paired comparisons, with the following objectives. The first objective is to establish fundamental information-theoretic limits of learning from a combination of paired and direct measurements under various statistical models. These fundamental limits also involve understanding the inherent tradeoffs between the two forms of measurements that accounts for the different noise and costs. The second objective is to develop scalable and computationally efficient algorithms whose performance attains these fundamental limits. The third objective is to transfer the theory to practice in applications such as material design using direct experimental and pairwise expert feedback.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
