Budget Feasible Procurement Mechanisms
[视频介绍]
简介:In this mini-course I will introduce and discuss the budget feasible mechanism design framework. This framework captures scenarios where the goal is to buy items or services from strategic agents under budget. The setting poses interesting combinatorial optimization problems with application domains that include crowdsourcing, marketing in social networks, recommendation systems, spectrum auctions, and privacy auctions.
We will focus on both the theory and the applications. The theoretical developments are concerned with combinatorial optimization, competitive analysis, approximation ratios, Bayesian models, posted price mechanisms, and leave many open questions. The applications we will discuss would be relevant to those interested in influence in social networks, pricing and matching crowdsourcing tasks, and privacy auctions.
The aim of the course is to provide a useful toolbox for procurement-related problems, as well as a good exposure to open problems for those interested in combinatorial optimization, mechanism design, and online learning.
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视频介绍
讲师:Nick
关键词:
课程简介:In this mini-course I will introduce and discuss the budget feasible mechanism design framework. This framework captures scenarios where the goal is to buy items or services from strategic agents under budget. The setting poses interesting combinatorial optimization problems with application domains that include crowdsourcing, marketing in social networks, recommendation systems, spectrum auctions, and privacy auctions.
We will focus on both the theory and the applications. The theoretical developments are concerned with combinatorial optimization, competitive analysis, approximation ratios, Bayesian models, posted price mechanisms, and leave many open questions. The applications we will discuss would be relevant to those interested in influence in social networks, pricing and matching crowdsourcing tasks, and privacy auctions.
The aim of the course is to provide a useful toolbox for procurement-related problems, as well as a good exposure to open problems for those interested in combinatorial optimization, mechanism design, and online learning.
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