Learning from Interaction
In this talk, I will present my research on developing natural language processing systems that learn from interacting in an environment. I will begin by describing the issues that arise when systems are trained on offline data and then deployed in interactive environments. Additionally, I will present an algorithm that addresses these issues using only environmental interaction without additional supervision. Moreover, I will demonstrate how learning from interaction can improve natural language processing systems. Finally, I will present a set of new interactive learning algorithms explicitly designed for natural language processing systems.
Bio: Kianté Brantley is a Postdoctoral Associate in the Department of Computer Science at Cornell University., working with Thorsten Joachims. He completed his Ph.D. in Computer Science at the University of Maryland College Park, advised by Dr. Hal Daumé III. His research focuses on developing machine learning models that can make automated decisions in the real world with minimal supervision. His research lies at the intersection of imitation learning, reinforcement learning, and natural language processing. He is a recipient of the NSF LSAMP BD Fellowship, ACM SIGHPC Computational and Data Science Fellowship, Microsoft Dissertation Research Grant, Ann G. Wylie Dissertation Fellowship, and NSF CIFellow Postdoctoral Fellowship.
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