Interactive Learning (IL) is a prominent machine learning paradigm that explores how intelligent autonomous systems learn to make improved sequential decisions through multiple rounds of interactions with the real world. IL-based systems have a wide range of applications including robotics, health-care, and marketing. However, for these systems to meet the practical requirements of the related application domains, there is a perennial need for developing efficient algorithms. In this talk, I will elaborate on how my work finds principled ways of designing such algorithms in the contexts of Reinforcement Learning and Domain Adaptation. I will present novel exploration/exploitation reinforcement learning algorithms for Partially Observable Markov Decision Processes (POMDP). Next, I will discuss a new algorithm for domain adaptation in the presence of shifts in the label distribution, which has applications in disease diagnosing and cloud service providing. I will conclude the talk with applications of IL in drone navigation, multi-agent swarm, recommendation systems, resource allocation, distributed optimization, and video games.
Kamyar Azizzadenesheli is a graduate student in the TensorLab at the University of California, Irvine, supervised by Prof. Anima Anandkumar. Currently, he is a visiting researcher at Caltech, hosted by Prof. Anima Anandkumar, working with ML and Control researchers at CMS department and the Center for Autonomous Systems and Technologies (CAST) in close collaboration with Prof. Yisong Yue, Prof. Soon-Jo Chung, and Prof. Joel W. Burdick. He is a former visiting researcher at Stanford University, hosted by Prof. Emma Brunskill, and researcher at Simons Institute, UC. Berkeley in Foundation of Machine Learning program. He is also a former guest researcher at INRIA France, hosted by Dr. Alessandro Lazaric as well as Microsoft Research Lab, New England, and New York.