Conference on Statistical Learning and Data Mining

关于“统计学习和数据挖掘”的国际会议将于2012年6月4日至7日在密歇根大学安娜堡校区举行。会议的目标是将来自学术界、工业界和政府的统计学习和数据挖掘领域的研究人员聚集在一起，营造一个轻松刺激的氛围，专注于统计学习理论、方法和应用的发展。会议将由国际知名的研究人员进行三次全体会议，他们的工作在统计学习和数据挖掘领域处于前沿地位。18个分组会议，每个会议有三个演讲，将涵盖对该领域非常感兴趣的其他主题。其中包括计算广告、函数估计、高维方法、结构化学习、图形模型、学习理论、模型选择、协方差估计、网络分析、计算生物学、信号和图像处理以及数据挖掘应用。还将有七场论文会议和两场海报会议，预计初级研究人员和研究生将参加。统计学习是一门相对较新的学科，从人工智能和多元统计的机器学习方法发展而来。统计学习的一般目标是发现、分类和预测，通常是在非常高的、实际上无限的维度环境中。功能强大的计算机的出现以及随之而来的海量数据集将这门学科带到了统计理论和实践的前沿。本次会议的主要目标是介绍该领域一些最重要的最新进展，并讨论未来的研究方向。会议的一个主要部分是将统计研究的领导者与学生、博士后研究员和年轻学者聚集在一个刺激的环境中。NSF的资金将主要支持美国大学的研究生和初级研究人员参加会议并发表演讲或海报。会议预计将加速统计学习和数据挖掘重要领域研究人员之间的互动和合作，从而导致新的和更有效的建模和推理方法的发展。

An international conference on "Statistical Learning and Data Mining" will be held June 4-7, 2012 on the Ann Arbor campus of the University of Michigan. The objective is to bring together researchers in statistical learning and data mining from academia, industry, and government in a relaxed and stimulating atmosphere focused on the development of statistical learning theory, methods and applications. The conference will feature three plenary talks by internationally prominent researchers whose work are cutting-edge in the field of statistical learning and data mining. Eighteen invited breakout sessions, each with three talks, will cover additional topics with great interest to the field. These include Computational Advertisement, Function Estimation, High-dimensional Methods, Structured Learning, Graphical Models, Learning Theory, Model Selection, Covariance Estimation, Network Analysis, Computational Biology, Signal and Image Processing and Data Mining Applications. There will also be seven contributed paper sessions and two contributed poster sessions where junior investigators and graduate students are expected to participate.Statistical learning is a relatively new discipline, evolving from machine learning methods of artificial intelligence and multivariate statistics. The general goals of statistical learning are discovery, classification and prediction, often in very high, effectively infinite, dimensional contexts. The advent of powerful computers with accompanying massive data sets has brought the discipline to the forefront of statistical theory and practice. The major goal of the proposed conference is to present some of the most important recent advances in the field and to discuss future research directions. A major part of the conference focuses on bringing statistical research leaders together with students, postdoctoral fellows, and young academics in a stimulating environment. The funding from the NSF will mainly support graduate students and junior researchers in American universities to attend the conference and present either a talk or a poster. The conference is expected to accelerate interactions and collaborations among researchers in the important area of statistical learning and data mining, and thereby lead to the development of new and more effective methods of modeling and inference.