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報(bào)告題目：一類魯棒的低秩半定矩陣學(xué)習(xí)方法

題目英文：Efficient Low-Rank Semidefinite Programming with Robust Loss Functions

主講人：胡恩良教授（云南師范大學(xué)數(shù)學(xué)學(xué)院教授）

時(shí)間：2024年1月4日（周四） 14:30 p.m.

形式：云南財(cái)經(jīng)大學(xué)卓遠(yuǎn)樓403會(huì)議室

主辦單位：信息學(xué)院

主持人：信息學(xué)院趙成貴副院長(zhǎng)

摘要： In real-world applications, it is important for machine learning algorithms to be robust against data outliers or corruptions. In this talk, I will focus on improving the robustness of a large class of learning algorithms that can be formulated as low-rank semidefinite programming 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  l ₁-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 several 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.

主講人簡(jiǎn)介：

胡恩良,，云南師范大學(xué)數(shù)學(xué)學(xué)院教授,、碩士生導(dǎo)師；中國(guó)計(jì)算機(jī)學(xué)會(huì)會(huì)員,、中國(guó)人工智能學(xué)會(huì)機(jī)器學(xué)習(xí)專委會(huì)通訊委員,、IEEE會(huì)員。博士畢業(yè)于南京航空航天大學(xué)計(jì)算機(jī)應(yīng)用技術(shù)專業(yè),，曾到香港科技大學(xué)做Research Assistant和Postdoctoral Fellow工作,，主要研究方向：機(jī)器學(xué)習(xí)中的大規(guī)模優(yōu)化計(jì)算理論及算法。已在CCF-A類國(guó)際會(huì)議International Conference on Machine Learning,、International Joint Conference on Artificial Intelligence,，和信息類核心期刊《IEEE Transactions on Pattern Analysis and Machine Intelligence 》、《IEEE Transactions on Neural Networks and Learning Systems》、《IEEE Transactions on Neural Networks》,、《Pattern Recognition》和《中國(guó)科學(xué): 信息科學(xué)》等上發(fā)表論文二十余篇,；主持研究國(guó)家自然科學(xué)基金項(xiàng)目三項(xiàng)。