问题：机械学习和理论经济学中的双层妄想应用

演讲人：张进博士，，，南方科技大学

主持人：朱希德博士，，，8188cc威尼斯治理学院

时间：2021年3月30日，，，下昼14:30

所在：8188cc威尼斯校本部东区1号楼治理学院467室

主理单位：8188cc威尼斯治理学院、8188cc威尼斯治理学院青年西席联谊会

演讲人简介：

    张进博士本科硕士均结业于大连理工大学，，，博士结业于加拿大维多利亚大学。。2015至2018年间任职于香港浸会大学，，，2019年头加入南方科技大学。。张进博士一直致力于优化理论和应用研究，，，主持多项国家级基金项目，，，代表性效果揭晓在Mathematical Programming、SIAM Journal on Optimization、SIAM Journal on Numerical Analysis、Journal of Machine Learning Research、International Conference on Machine Learning等有主要影响力的运筹优化、机械学习期刊与聚会上。。张进博士的研究效果获得2020年第七届中国运筹学会青年科技奖，，，入选2021年深圳市优异科技立异人才作育优异青年妄想。。

演讲内容简介：

In this talk, we will discuss some recent advances in the applications of Bi-Level Programming Problem (BLPP). First, we study a gradient-based bi-level optimization method for learning tasks. In particular, by formulating bi-level models from the optimistic viewpoint and aggregating hierarchical objective information, we establish Bi-level Descent Aggregation (BDA), a flexible and modularized algorithmic framework for BLPP. Extensive experiments justify our theoretical results and demonstrate the superiority of the proposed BDA for different tasks, including hyper-parameter optimization and meta learning. Second, we propose a sufficient condition in the form of a partial error bound condition which guarantees the partial calmness condition. Our main result states that the partial error bound condition for the combined programs based on B and FJ conditions are generic for an important setting with applications in economics and hence the partial calmness for the combined program is not a particularly stringent assumption. Moreover we derive optimality conditions for the combined program for the generic case without any extra constraint qualifications.

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