Learning from Interaction

Machine learning systems have seen advancements due to large models pre-trained on vast amounts of data. These pre-trained models have led to progress on various downstream tasks when fine-tuned. However, for machine learning systems to function in real-world environments, they must overcome certain challenges that are not influenced by model or dataset sizes. One potential solution is to fine-tune machine learning models based on online interactions.

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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