Clean water and clean air are vital for public health. This project focuses on developing high-efficiency and environmentally sustainable filters for removing harmful air/water pollutants. The team has developed novel architectures and functionalities for the filters to achieve high permeance, high removal efficiency, and excellent reusability.
All members of the HKU community and the general public are welcome to join!
Speaker: Dr Zhiyi Huang, Associate Professor, Department of Computer Science, Faculty of Engineering, HKU
Moderator: Dr. Yue Chen, Associate Professor, Department of Mechanical Engineering, Faculty of Engineering, HKU
Date: 18th May 2023 (Thursday)
Mode: Mixed (both face-to-face and online). Seats for on-site participants are limited. A confirmation email will be sent to participants who have successfully registered.
The design of optimal auctions for revenue maximization is a central topic in Economics. Classical optimal auction theory assumes that bidders’ values are drawn from a known distribution. In reality, the source of such prior information is really past data. Cole and Roughgarden (2014) modeled past data as i.i.d. samples from the value distribution and asked: How many samples are sufficient/necessary to learn a near optimal auction? This TechTalk will introduce a unified theory that yields sample-efficient algorithms with optimal sample complexity for auctions with homogeneous goods, and state-of-the-art sample complexity for auctions with heterogeneous goods. Unlike conventional statistical learning theory which focuses on the complexity of hypothesis classes, our new theory relies on the simplicity of data distributions and a monotonicity property of these problems.