Wednesday April 13, 11am-12pm, 1116-E Klaus
Communication Efficient Distributed Agnostic Boosting
Shang-Tse (Shang) Chen
Advisor: Prof. Polo Chau and Prof. Nina Balcan
In this work, we consider the problem of learning from distributed data in the agnostic setting, i.e., in the presence of arbitrary forms of noise. Our main contribution is a general distributed boosting-based procedure for learning an arbitrary concept space, that is simultaneously noise tolerant, communication efficient, and computationally efficient. This improves significantly over prior works that were either communication efficient only in noise-free scenarios or computationally prohibitive. Empirical results on large synthetic and real-world datasets demonstrate the effectiveness and scalability of the proposed approach.
Shang-Tse (Shang) Chen is a 3rd-year Ph.D. student in Computer Science, working with Profs. Polo Chau and Nina Balcan. Shang got his BS in CS from National Taiwan University, and after that he spent two years as a research assistant at Academia Sinica in Taiwan. Shang's current research interests are in applying machine learning and game theory techniques to security problems.