Colloquium: Efficient and Robust Interactive Learning
Abstract: Different from traditional machine learning, interactive learning allows a learner to be involved in the data collection process through interacting with the environment. An interactive learner can carefully avoid collecting redundant information, thus being able to make accurate predictions with a small amount of data. Two key questions in interactive learning research are of central interest: first, can we design and analyze interactive learning algorithms that have data efficiency, computational efficiency and robustness guarantees? second, can we identify novel interaction models which learners can benefit from? In this talk, I will answer both questions in the affirmative. In the first part of the talk, I will present our work on efficient noise-tolerant active learning of linear classifiers with near-optimal label requirements. In the second part, I will describe new algorithms and tools for contextual bandit learning with continuous action spaces. In the last part, I will discuss a new interactive learning model, namely warm-starting contextual bandits, and present an algorithm in this model with robustness guarantees. I will conclude my talk by outlining several promising directions for future research.
Bio: Chicheng Zhang is a postdoctoral researcher at the machine learning group at Microsoft Research New York City. He received a Bachelor degree from Peking University in 2012 and a PhD in Computer Science from UC San Diego in 2017. Chicheng has broad research interests in machine learning, including but not limited to active learning, contextual bandits, unsupervised learning and confidence-rated prediction. His main research interests lie in the design and analysis of interactive machine learning algorithms.