We are in the middle of the AI revolution with the success of AlphaGo, image classification, and machine translation. However, the success relies on a large amount of data, which raises numerous challenges for novel tasks since the data is usually not readily-available and takes money and time to collect. How can we minimize the data collection costs and train models efficiently with insufficient data? In this talk, I will talk about novel adaptive data collection and learning algorithms arising from the so-called multi-armed bandit framework and show their theoretical guarantees and their effectiveness in real-world applications. Specifically, I will show that my algorithms can quickly recommend personalized products to a novel user in a scalable way via a novel extension of online optimization algorithms. I will also discuss how biological experiments can be performed with a reduced amount of budget by adaptively selecting what experiments to run next.
Kwang-Sung Jun is a postdoctoral researcher at the University of Wisconsin-Madison Wisconsin Institute for Discovery, advised by Profs. Robert Nowak, Rebecca Willett, and Stephen Wright. His research focuses on adaptive and interactive machine learning that arises in real-world and interdisciplinary applications. Specifically, he works on multi-armed bandits, online optimization, and cognitive modeling, which has applications in personalized recommendation, adaptive biological experiments, and psychology. He received a Ph.D. in Computer Science from the University of Wisconsin-Madison under the supervision of Prof. Xiaojin (Jerry) Zhu.