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2014年12月12-14日 北京 · 新云南皇冠假日酒店

2014中国大数据技术大会

暨第二届CCF大数据学术会议

首页 > 演讲嘉宾 > 演讲嘉宾详情> Eric P. Xing
Eric P. Xing

Eric P. Xing

Professor of Carnegie Mellon University Program Chair of ICML 2014

Dr. Eric Xing is a Professor of Machine Learning in the School of Computer Science at Carnegie Mellon University. His principal research interests lie in the development of machine learning and statistical methodology; especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in social and biological systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley. His current work involves, 1) foundations of statistical learning, including theory and algorithms for estimating time/space varying-coefficient models, sparse structured input/output models, and nonparametric Bayesian models; 2) computational and statistical analysis of gene regulation, genetic variation, and disease associations; and 3) large-scale systems for machine learning. Professor Xing has published over 200 peer-reviewed papers, and is an associate editor of the Annals of Applied Statistics (AOAS), the Journal of American Statistical Association (JASA), the IEEE Transaction of Pattern Analysis and Machine Intelligence (PAMI), the PLoS Journal of Computational Biology, and an Action Editor of the Machine Learning Journal (MLJ), the Journal of Machine Learning Research (JMLR). He is a member of the DARPA Information Science and Technology (ISAT) Advisory Group, a recipient of the NSF Career Award, the Sloan Fellowship, the United States Air Force Young Investigator Award, the IBM Open Collaborative Research Award, and best paper awards in a number of premier conferences including UAI, ACL, EMNLP, SDM, ISMB. He is the Program Chair of ICML 2014.

演讲主题:A New Platform for Cloud-based Distributed Machine Learning on Big Data

In many modern applications such as web-scale content extraction via topic models, genome-wide association mapping via sparse regression, and image understanding via deep neural networks, one needs to handle BIG machine learning (ML) problems that threaten to exceed the limit of current architectures and algorithms. While several new system frameworks beyond Hadoop, notably Spark and GraphLab, have emerged for parallelizing ML programs, good dialogs between system and ML remain difficult --- most system designs are agnostic to the distinctive characteristics of ML programs, treating them literally as operation sets as in traditional programs instead of iterative convergent procedures for optimizing a function, and hence ignore important properties thereof, such as error tolerance, non-uniform convergence, and structural coupling, which can fundamentally influence the priorities and goals for system design and open up new opportunities for improving efficiency. In this talk, I will discuss these opportunities and present a new framework, Petuum, for distributed machine learning that leverages these opportunities, and demonstrate how system innovations in light of ML-first principles lead to multiple orders of magnitude of scalability on a modest lab cluster for a wide range of large scale problems in text modeling (topic model with 1M topics), social network (mixed-membership inference on 100M node), personalized genome medicine (sparse regression on 100M dimensions), and computer vision (deep neural network with billions of parameters), with provable guarantee on correctness of distributed inference.

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中国计算机学会

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