Thursday, 22 September at 15:00, room A707.
Abstract: Wasserstein distance, which measures the discrepancy between distributions, shows efficacy in various types of natural language processing (NLP) and computer vision (CV) applications. One of the challenges in estimating Wasserstein distance is that it is computationally expensive and does not scale well for many distribution comparison tasks. In this talk, I propose a learning-based approach to approximate the 1-Wasserstein distance with trees. Then, I demonstrate that the proposed approach can accurately approximate the original 1-Wasserstein distance for NLP tasks. (https://arxiv.org/abs/2206.12116)
Speaker’s biography: Makoto Yamada received the Ph.D. degree in statistical science from The Graduate University for Advanced Studies (SOKENDAI, The Institute of Statistical Mathematics), Tokyo, in 2010. Currently, he is a team leader at RIKEN AIP, an associate professor at Kyoto University, and a transitional associate professor at Okinawa Institute of Science and Technology (OIST). His research interests include machine learning and its application to biology, natural language processing, and computer vision. He published more than 50 research papers in premium conferences and journals such as NeurIPS, AISTATS, ICML, AAAI, IJCAI, and TPAMI, and won the WSDM 2016 Best Paper Award.