Yuu Jinnai

According to our database, Yuu Jinnai authored at least 22 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding.

Yuu Jinnai, Ukyo Honda, Tetsuro Morimura, Peinan Zhang

CoRR, 2024

Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decoding.

Yuu Jinnai, Kaito Ariu

CoRR, 2024

2023

Model-Based Minimum Bayes Risk Decoding.

Yuu Jinnai, Tetsuro Morimura, Ukyo Honda, Kaito Ariu, Kenshi Abe

CoRR, 2023

On the Depth between Beam Search and Exhaustive Search for Text Generation.

Yuu Jinnai, Tetsuro Morimura, Ukyo Honda

CoRR, 2023

Blind Signal Separation for Fast Ultrasound Computed Tomography.

Takumi Noda, Yuu Jinnai, Naoki Tomii, Takashi Azuma

CoRR, 2023

2021

Lipschitz Lifelong Reinforcement Learning.

Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Exploration in Reinforcement Learning with Deep Covering Options.

Yuu Jinnai, Jee Won Park, Marlos C. Machado, George Dimitri Konidaris

Proceedings of the 8th International Conference on Learning Representations, 2020

Neural Architecture Search Using Deep Neural Networks and Monte Carlo Tree Search.

Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search.

Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca

CoRR, 2019

Discovering Options for Exploration by Minimizing Cover Time.

Yuu Jinnai, Jee Won Park, David Abel, George Dimitri Konidaris

Proceedings of the 36th International Conference on Machine Learning, 2019

Finding Options that Minimize Planning Time.

Yuu Jinnai, David Abel, David Ellis Hershkowitz, Michael L. Littman, George Dimitri Konidaris

Proceedings of the 36th International Conference on Machine Learning, 2019

State Abstraction as Compression in Apprenticeship Learning.

David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Finding Options that Minimize Planning Time.

Yuu Jinnai, David Abel, Michael L. Littman, George Dimitri Konidaris

CoRR, 2018

AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search.

Linnan Wang, Yiyang Zhao, Yuu Jinnai

CoRR, 2018

Policy and Value Transfer in Lifelong Reinforcement Learning.

David Abel, Yuu Jinnai, Sophie Yue Guo, George Dimitri Konidaris, Michael L. Littman

Proceedings of the 35th International Conference on Machine Learning, 2018

Parallel A* for State-Space Search.

Alex Fukunaga, Adi Botea, Yuu Jinnai, Akihiro Kishimoto

Proceedings of the Handbook of Parallel Constraint Reasoning., 2018

2017

On Hash-Based Work Distribution Methods for Parallel Best-First Search.

Yuu Jinnai, Alex Fukunaga

J. Artif. Intell. Res., 2017

A Survey of Parallel A.

Alex Fukunaga, Adi Botea, Yuu Jinnai, Akihiro Kishimoto

CoRR, 2017

Learning to Avoid Dominated Action Sequences in Planning for Black-Box Domains.

Yuu Jinnai, Alex Fukunaga

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

Learning to Prune Dominated Action Sequences in Online Black-Box Planning.

Yuu Jinnai, Alex S. Fukunaga

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Automated Creation of Efficient Work Distribution Functions for Parallel Best-First Search.

Yuu Jinnai, Alex S. Fukunaga

Proceedings of the Twenty-Sixth International Conference on Automated Planning and Scheduling, 2016

Abstract Zobrist Hashing: An Efficient Work Distribution Method for Parallel Best-First Search.

Yuu Jinnai, Alex Fukunaga

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016