Conference on Nonconvex Statistical Learning, University of Southern California, May 26-27, 2017

为期两天的非凸统计学习跨学科会议将于2017年5月26日（星期五）和5月27日（星期六）在南加州大学校园举行。会议网站：https://sites.google.com/a/usc.edu/cnsl2017/home将在会前不断更新，并将提供一个供普遍查阅的会议讲座资料库。 在当今的数字世界中，大量的数据，即，大数据，几乎可以在科学研究的各个方面和人类活动的各行各业中找到。 需要有效地管理这些数据，以便进行可靠的预测、推断和改进决策。 统计学习是一门新兴的科学学科，其中数学建模，计算算法和统计分析联合使用来解决这样的数据管理问题。 会议的目的是汇集来自多个学科的各级研究人员，包括计算和应用数学，优化，统计和工程，以报告会议主题的最新进展，并为其进一步发展交换意见。将促进参与者之间的合作，目标是推进统计学习领域的科学，并促进所涉学科之间的相互作用。会议的形式包括由该领域的专家研究人员提供的大约二十多个讲座。 讲座之间的休息时间安排为所有参与者之间的讨论，包括研究生，博士后研究员，学术界和工业界的研究人员以及大学的教职员工。该奖项为初级参与者的差旅费提供支持。 目前，凸优化已成为解决统计学习中许多问题的主要途径。 然而，有越来越多的证据支持使用非凸公式，以提高模型的真实性和改善其推广。 在计算统计、压缩传感、成像科学、机器学习、生物信息学和投资组合选择等领域，非凸泛函被用来表达模型损失、促进稀疏性和增强鲁棒性，取得了上级结果和新进展。本次会议为与会者提供了一个论坛，报告他们的研究和交流思想有关的非凸泛函在统计学习中的使用。 主题分为四个主要部分：建模，计算进展，大数据统计学习和创新应用。 讲座将涵盖理论和算法以及未来研究的方向。

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.