Fast Optimal Transport and Applications to Inference and Simulation in Large Scale Statistical Machine Learning

2016 Fiscal Year Final Research Report

• Project/Area Number: 26700002
• Research Category: Grant-in-Aid for Young Scientists (A)
• Allocation Type: Partial Multi-year Fund
• Research Field: Statistical science
• Research Institution: Kyoto University
• Principal Investigator: CUTURI Marco 京都大学, 情報学研究科, 准教授 (80597344)
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: 最適輸送理論 / 機械学習 / 最適化 / グラフィックス

Outline of Final Research Achievements

This funding was used to push forward the idea that optimal transport could be used numerically to solve real life problems using a regularization approach. We have demonstrated over the course of this project that these ideas were feasible, and have shown their applicability to a very wide range of applications, ranging from graphics and medical imaging to graphics and machine learning. These ideas were presented in top conferences and journals.

Free Research Field

統計学