A paper coauthored by Computational Learning Theory Team Leader Kohei Hatano of the RIKEN Center for Advanced Intelligence Project (AIP) won the Best Paper Award at the 12th International Conference and Workshops on Algorithms and Computation (WALCOM 2018), which took place in Dacca, Bangladesh. The winning paper was presented at the conference on March 5.
Paper
Paper: Boosting over non-determinist ZDDs, by Takahiro Fujita, Kohei Hatano and Eiji Takimoto
Summary: We show that one of machine learning techniques called “boosting” can be efficiently computed over compressed data using non-determinist Zero-Suppressed Binary Decision Diagrams (ZDDs). A big advantage of this scheme is the ability to perform analysis using big data with minimal space. An interesting point in this research is that the combinatorial online prediction, a seemingly unrelated machine learning concept is successfully adopted for utilizing compressed data.
