The two-day interdisciplinary Conference on Nonconvex Statistical Learning takes place at the campus of the University of Southern California on Friday, May 26, and Saturday, May 27, 2017. The website of the conference: https://sites.google.com/a/usc.edu/cnsl2017/home will be continuously updated prior to the conference and will provide a repository for the lectures of the meeting to be made available generally. In today's digital world, huge amounts of data, i.e., big data, can be found in almost every aspect of scientific research and every walk of human activities. These data need to be managed effectively for reliable prediction, inference, and improved decision making. Statistical learning is an emergent scientific discipline wherein mathematical modeling, computational algorithms, and statistical analysis are jointly employed to address such a data management problem. The aim of the conference is to bring together researchers at all levels from multiple disciplines, including computational and applied mathematics, optimization, statistics, and engineering to report on the state of the art of the conference subject and exchange ideas for its further development. Collaborations among the participants will be fostered with the goal of advancing the science of the field of statistical learning and promoting the interfaces of the involved disciplines. The format of the conference consists of roughly two dozen lectures given by expert researchers of the field. Break times in-between the lectures are scheduled to allow discussions among all participants who will include graduate students, postdoctoral fellows, researchers in academia and industry, and faculty members in universities. This award provides support targeted for the travel expenses of junior participants.
Till now, convex optimization has been a principal venue for solving many problems in statistical learning. Yet there is increasing evidence supporting the use of nonconvex formulations to enhance the realism of the models and improve their generalizations. Superior results and new advances have occurred in areas such as computational statistics, compressed sensing, imaging science, machine learning, bio-informatics and portfolio selection in which nonconvex functionals are employed to express model loss, promote sparsity, and enhance robustness. This conference provides a forum for the participants to report on their research and exchange ideas pertaining to the use of nonconvex functionals in statistical learning. The topics are organized in four main streams: modeling, advances in computation, big-data statistical learning, and innovative applications. The lectures will cover both theory and algorithms as well as promising directions for further research.
