报告题目：Efficient Low-Rank Semidefinite Programming with Robust Loss Functions

报告人：胡恩良教授 云南师范大学

摘要：In real-world applications, it is important for machine learning algorithms to be robust against data outliers or corruptions. In this paper, we focus on improving the robustness of a large class of learning algorithms that are formulated as low-rank semi-definite programming (SDP) problems. Traditional formulations use the square loss, which is notorious for being sensitive to outliers. We propose to replace this with more robust noise models, including the l1-loss and other nonconvex losses. However, the resultant optimization problem becomes difficult as the objective is no longer convex or smooth. To alleviate this problem, we design an efficient algorithm based on majorization-minimization. The crux is on constructing a good optimization surrogate, and we show that this surrogate can be efficiently obtained by the alternating direction method of multipliers (ADMM). By properly monitoring ADMM’s convergence, the proposed algorithm is empirically efficient and also theoretically guaranteed to converge to a critical point. Extensive experiments are performed on four machine learning applications using both synthetic and real-world data sets. Results show that the proposed algorithm is not only fast but also has better performance than the state-of-the-arts.

报告时间：2022年12月15日，星期四 下午15:00

会议形式：腾讯会议

会议ID： 542-584-338

专家简介：胡恩良，云南师范大学教授、硕士生导师；IEEE会员、中国计算机学会会员、中国人工智能学会机器学习专业委员会委员。博士毕业于南京航空航天大学计算机应用技术专业，曾到香港科技大学做过Rescarch Assistant和Postdoctoral Fellow工作。在知名国际会议ICML、IJCAL和期刊IEEE TPAMI、IEEE TNNLS、SCIENCE CHINA等发表学术论文20余篇，目前已主持国家自然科学基金项目3项。