Abstract

Researchers from across the business, machine learning and social science are increasingly designing algorithms to address developing challenges. In this talk, I examine two burgeoning directions for the developing world: coreset construction and fairness in automated decision-making tasks.

With the prevalence of automatic information extraction/integration systems and predictive machine learning algorithms in numerous application areas, we are faced with a huge volume of (potentially uncertain) data. This has led to a surge of interests in data summarization from several research communities including theoretical computer science, databases, machine learning. We investigate a popular approach of data summarization, called coreset construction, which can help manage, analyze and optimize over big datasets. In this talk, I will introduce how to construct coresets for different types of data, e.g., uncertain data or data with bounded doubling dimension.

Automated decision-making algorithms are increasingly deployed and affect people’s lives significantly. Recently, there has been growing concern about systematically discriminate against particular groups of individuals that may exist in such algorithms. I will first introduce the motivations of fairness requirements in the real-world applications. Then I will introduce our recent progress in designing algorithms that maintain fairness requirements for two important decision-making tasks: multiwinner voting and classification.

Time

8月2日（周四）10:00-11:00

Speaker

Lingxiao Huang is a postdoc of computer science in EPFL, where he is advised by Nisheeth Vishnoi. He joined EPFL in 2017, after received his Ph.D. in IIIS, Tsinghua University.

His current research interest is algorithm design in machine learning and social science. He is passionate about creating novel algorithms that are motivated by existing practical challenges.

Venue

信息管理与工程学院  602会议室

上海财经大学

上海市杨浦区武东路100号