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                  Advanced Topics in Multi-label Learning
                  时间:2019-12-03    浏览量:


                  报告题目: Advanced Topics in Multi-label Learning

                  人:刘威威,武汉大学计算机学院

                  报告时间:2019126日(星期五) 上午10:00

                  报告地点:重庆大学A区主教学大楼1811

                  报告摘要: Multi-label learning, in which each instance can belong to multiple labels simultaneously, has significantly attracted the attention of researchers as a result of its wide range of applications, which range from document classification and automatic image annotation to video annotation. Many multi-label learning models have been developed to capture label dependency. Amongst them, the classifier chain (CC) model is one of the most popular methods due to its simplicity and promising experimental results. However, CC suffers from three important problems: Does the label order affect the performance of CC? Is there any globally optimal classifier chain which can achieve the optimal prediction performance for CC? If yes, how can the globally optimal classifier chain be found? It is non-trivial to answer these problems. Another important branch of methods for capturing label dependency is encoding-decoding paradigm. Based on structural SVMs, maximum margin output coding (MMOC) has become one of the most representative encoding-decoding methods and shown promising results for multi-label classification. Unfortunately, MMOC suffers from two major limitations: 1) Inconsistent performance. 2) Prohibitive computational cost. Therefore, it is non-trivial to break the bottlenecks of MMOC, and develop efficient and consistent algorithms for solving multi-label learning tasks. The prediction of most multi-label learning methods either scales linearly with the number of labels or involves an expensive decoding process, which usually requires solving a combinatorial optimization. Such approaches become unacceptable when tackling thousands of labels. It is imperative to design an efficient, yet accurate multi-label learning algorithm with the minimum number of predictions. This report systematically shows how to solve aforementioned issues.


                  主讲人介绍:刘威威, 武汉大学计算机学院, 教授、博导,担任院长助理。20178月于悉尼科技大学(University of Technology Sydney, UTS)获得博士学位,导师Ivor W.Tsang教授。主要研究方向为人工智能、机器学习,包括多标签学习、聚类、特征选择、稀疏学习和深度学习等。目前,已在世界顶级期刊及会议上发表CCF A类第一作者学术论文12篇,其中,包括机器学习旗舰型期刊Journal of Machine Learning Research (JMLR),模式识别、计算机视觉和机器学习应用顶级期刊IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),机器学习顶级学术会议NIPS、ICML,人工智能顶级学术会议AAAI、IJCAI。2018年,入选第十五批国家青年千人计划,入选美国电气与电子工程师协会IEEE Senior Member,担任神经网络顶级期刊IEEE Transactions on Neural Networks and Learning SystemsTNNLS)主要客座编辑(Leading Guest Editor)。担任人工智能顶会IJCAI2020的高级程序委员(Senior Program Committee ),以及机器学习顶级学术会议ICLR2019的领域主席(Area Chair)。








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