演算式決策與學習專題

Algorithmic game theory and learning are active interdisciplinary research areas with contributions from theoretical computer science, economics, networking, artificial intelligence, operations research, and discrete mathematics. In this course, we aim to further look into the rich literature and most updated developments or progress by reviewing the papers from the top conferences and journals related to the areas, and expect students to form their own research proposals.

Economics and Computation, or Algorithmic Decision and Learning, or Algorithmic Mechanism Design and Social Choice

Literature review: paper reading, presentation, and critique multiple times (60%) Research Proposal and presentation (40%)

• Algorithmic Bias
• Feature Selection/Engineering
• Multiagent Learning
• Medical/Health Data Analysis and/or scheduling
• Research proposal presentation

Conference papers mainly from ACM Conference on Economics and Computation, Conference on Web and Internet Economics, International Conference on Autonomous Agents and Multiagent Systems, Symposium on Algorithmic Game Theory, Symposium on Theory of Computing, Symposium on Foundation of Computer Science, SIAM Symposium on Discrete Algorithms, Conference on Learning Theory, International Conference on Machine Learning, Conference on Neural Information Processing, etc. Journal papers mainly from Games and Economic Behavior, International Journal of Game Theory, ACM Transactions on Economics and Computation, etc.

地點

MB301

時間

By appointment

聯絡方式

poanchen@nctu.edu.tw