
Shinji Ito (Ph.D.)
Title
                            Team Director
Members
- 
                                        Team directorShinji Ito
 - 
                                        Visiting scientistJunya Honda
 - 
                                        Visiting scientistHan Bao
 - 
                                        Visiting scientistTaira Tsuchiya
 - 
                                        Visiting scientistJunpei Komiyama
 - 
                                        Visiting scientistKazushi Tsutsui
 - 
                                        Visiting scientistShinsaku Sakaue
 - 
                                        Visiting scientistTasuku Soma
 - 
                                        Visiting scientistTaihei Oki
 - 
                                        Part-time worker IIYuji Tamakoshi
 - 
                                        Part-time worker IIYuki Shibukawa
 
Introduction
The sequential decision-making team works to develop algorithms and theories for making rational decisions in a sequential manner in the face of forecast uncertainty and environmental fluctuations. In recent years, along with the evolution of information technology, there has been a demand for technology to make rational decisions based on the large amount of data being generated in real-time in today's world. To meet this challenge, we promote research related to online learning, bandit problems, and reinforcement learning, aiming to understand effective decision-making algorithms in a fluctuating environment and to construct and extend theoretical systems that support such algorithms.
Main Research Field
                            Informatics
                        
                            Research Field
                            Theory of informatics / Mathematical informatics / Intelligent informatics
                        
                            Research Subjects
                            Sequential decision-making
Online learning
Bandit problems
Reinforcement learning
Learning theory
                        Online learning
Bandit problems
Reinforcement learning
Learning theory
Poster(s)
- FY2024 Research Results(PDF 2MB) (Japanese version)
 - FY2023 Research Results(PDF 3MB) (Japanese version)
 
Research Achievements
Related posts
                        posted on October 17, 2025 15:19Seminar
                    
                    
                
                        posted on September 26, 2025 14:53Information
                    
                    
                
                        posted on July 11, 2025 16:28Seminar
                    
                    
                
                        posted on May 12, 2025 10:41Information
                    
                    
                
                        posted on May 12, 2025 10:12Information
                    
                    
                
                        posted on January 27, 2025 15:11Information
                    
                    
                
                        posted on January 27, 2025 13:54Information
                    
                    
                
                        posted on December 18, 2024 11:11Seminar
                    
                    
                
                        posted on October 2, 2024 17:33Information
                    
                    
                
                        posted on May 15, 2024 17:34Information
                    
                    
                
                        posted on May 9, 2024 22:55Information
                    
                    
                
                        posted on April 30, 2024 18:24Information
                    
                    
                
                        posted on December 15, 2023 10:39Information
                    
                    
                
