2 citations · 2 across the 6 of their papers we have counts for
6 papers
Understanding Federated Learning Through the Lens of Mechanism Design: The Role of Data Heterogeneity
Lina Alkarmi, Po-Yen Chen, Mingyan Liu
Federated learning (FL) requires effective incentive mechanisms to motivate data sharing and prevent strategic free-riding. Recent FL mechanisms such as the Shapley value mechanism…
Sequential Strategic Classification with Multi-Stage Selective Classifiers
Ziyuan Huang, Lina Alkarmi, Mingyan Liu
Strategic classification studies the problem where self-interested individuals or agents manipulate their response to obtain favorable decision outcomes made by classifiers, typica…
Multi-Level Strategic Classification: Incentivizing Improvement through Promotion and Relegation Dynamics
Ziyuan Huang, Lina Alkarmi, Mingyan Liu
Strategic classification studies the problem where self-interested individuals or agents manipulate their response to obtain favorable decision outcomes made by classifiers, typica…
When In Doubt, Abstain: The Impact of Abstention on Strategic Classification
Lina Alkarmi, Ziyuan Huang, Mingyan Liu
Algorithmic decision making is increasingly prevalent, but often vulnerable to strategic manipulation by agents seeking a favorable outcome. Prior research has shown that classifie…
Realigning Incentives to Build Better Software: a Holistic Approach to Vendor Accountability
Gergely Biczók, Sasha Romanosky, Mingyan Liu
In this paper, we ask the question of why the quality of commercial software, in terms of security and safety, does not measure up to that of other (durable) consumer goods we have…
Incentivizing Secure Software Development: the Role of Voluntary Audit and Liability Waiver
Ziyuan Huang, Gergely Biczók, Mingyan Liu
Misaligned incentives in secure software development have long been the focus of research in the economics of security. Product liability, a powerful legal framework in other indus…