Speaker 1: Dr. Makoto Yamada, Associate Professor, OIST

Title: Approximating 1-Wasserstein Distance with Trees

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.

Speaker 2: Ms. Terezie Sedlinska, PhD Student, OIST

Title:  Reinforcement learning behavioral modeling: Two studies of Pavlovian and operant valuation in humans and rats