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学术报告(7月5日)

发布日期:2017-07-05

 

报告人盛胜利 博士

题目Data Quality and Data Mining with Crowdsourcing

时间:2017年7月5日 15:00

地点:理工楼321

摘要Crowdsourcing systems provide convenient platforms to collect human intelligence for a variety of tasks (e.g., labeling objects) from a vast pool of independent workers (a crowd). Compared with traditional expert labeling methods, crowdsourcing is obviously more efficient and cost-effective, but the quality of a single labeler cannot be guaranteed. In taking advantage of the low cost of crowdsourcing, it is common to obtain multiple labels per object (i.e., repeated labeling) from the crowd. In this talk, we outline our research on crowdsourcing from three aspects: (1) crowdsourcing mechanisms, specifically on repeated labeling strategies; (2) ground truth inference, specifically on noise correction after inference and biased wisdom of the crowd; and (3) learning from crowdsourced data.

We first present repeated-labeling strategies of increasing complexity to obtain multiple labels. Repeatedly labeling a carefully chosen set of points is generally preferable. A robust technique that combines different notions of uncertainty to select data points for more labels is recommended. Recent research on crowdsourcing focuses on deriving an integrated label from multiple noisy labels via expectation-maximization based (EM-based) ground truth inference. We present a novel framework that introduces noise correction techniques to further improve the label quality of the integrated labels obtained after ground truth inference. We further show that biased labeling is a systematic tendency. State-of-the-art ground truth inference algorithms cannot handle the biased labeling issue very well. Our simple consensus algorithm performs much better. Finally, we present pairwise solutions for maximizing the utility of multiple noisy labels for learning. Pairwise solutions can completely avoid the potential bias introduced in ground truth inference. They have both sides (potential correct and incorrect/noisy information) considered, so that they have very good performance whenever there are a few or many labels available.

报告人简介:

盛胜利(VICTOR S. SHENG)是美国阿肯色中央大学计算机科学系副教授和数据分析实验室主任,1999年7月于苏州大学获硕士学位,2003年12月于加拿大新不伦瑞克大学获硕士学位,2007年8月于加拿大西安大略大学获博士学位,2007年9月至2009年8月间于美国纽约大学斯特恩商学院做博士后研究员。研究领域为数据挖掘与机器学习、人工智能、数据安全和决策支持,及其在商业、生物信息学、医疗信息学、软件工程等领域的应用。

主持和参与美国自然科学基金、加拿大自然科学与工程研究基金10余项;在国际学术会议和期刊上共发表论文100多篇,其中CCF推荐的A类期刊和会议论文20余篇,单篇论文被引用最高达720余次。2015年荣获WISE最佳学生论文奖finalist; 2011年荣获ICDM大会最佳论文奖; 2008年荣获KDD大会最佳论文奖亚军; 2008年机器学习研讨会Google学生奖获得者; 2006年荣获IEEE Kitchener-Waterloo Section知识和数据挖掘联合研讨会最佳海报奖。

研究成果多次发表在数据挖掘和机器学习的顶级会议和期刊上, 国际学期刊包括TPAMI, TKDE, JMLR, TMM, TNNLS和DMKD等。国际学术会议包括IJCAI, KDD, ICML, AAAI, ECML, ICDM, DASFAA, ACM MM, ICMR, ICME, CIKM等。现任ICDM 2017的financial Chair和多个国际期刊编委。多次担任美国国家科学基金会评审委员会委员和国际学术会议分会主席。并多次在多家高级国际学术会议和期刊担任评审委员会委员。