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My resume

Yasuhiro Fujita

• Email: [e-mail]

Research interests

• Reinforcement learning

Publications

Google Scholar

• T. Xu, Y. Fujita, and E. Matsumoto, “Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis,” CVPR, 2022. arXiv
• Y. Fujita, P. Nagarajan, T. Kataoka, and T. Ishikawa, “ChainerRL: ChainerRL: A Deep Reinforcement Learning Library,” Journal of Machine Learning Research, 22(77), 1-14. arXiv code
• Y. Fujita, K. Uenishi, A. Ummadisingu, P. Nagarajan, S. Masuda, and M. Y. Castro, “Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators,” IROS, 2020. arXiv
• Y. Fujita, T. Kataoka, P. Nagarajan, and T. Ishikawa, “ChainerRL: A Deep Reinforcement Learning Library,” in NeurIPS Deep Reinforcement Learning Workshop, 2019. arXiv code
• A. Havens, Y. Ouyang, P. Nagarajan, and Y. Fujita, “Learning Latent State Spaces for Planning through Reward Prediction,” in NeurIPS Deep Reinforcement Learning Workshop, 2019. arXiv
• Y. Nagano, S. Yamaguchi, Y. Fujita, and M. Koyama, “A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning,” in ICML, 2019. arXiv
• M. Miyashita, S. Maruyama, Y. Fujita, M. Kusumoto, T. Pfeiffer, E. Matsumoto, R. Okuta, and D. Okanohara, “Toward Onboard Control System for Mobile Robots via Deep Reinforcement Learning,” in NeurIPS Deep RL Workshop, 2018. pdf
• J. Rothfuss, I. Clavera, J. Schulman, Y. Fujita, T. Asfour, and P. Abbeel, “Model-Based Reinforcement Learning via Meta-Policy Optimization,” in CoRL, 2018. arXiv
• Y. Fujita and S. Maeda, “Clipped Action Policy Gradient,” in ICML, 2018. arXiv code slides

Translations

• Co-translated into Japanese Algorithms of Reinforcement Learning -> 速習 強化学習 ―基礎理論とアルゴリズム―
• Co-translated into Japanese Reinforcement Learning: An Introduction (second edition) -> 強化学習 (第2版)

Code

• ChainerRL: A deep RL library in Python and Chainer
• PFRL: A deep RL library in Python and PyTorch
• async-rl: An A3C implementation in Python and Chainer
• DQN-in-the-Caffe: A DQN implementation in C++ and Caffe

Work experience

• Engineer at Preferred Networks, Inc. (April 2015 - Present)

  • Research and development in machine learning for industrial applications: autonomous driving, robotics, computer graphics, and quantitative finance.
  • OSS development for reinforcement learning: ChainerRL and PFRL.

Professional activities

• Program committee: Deep Reinforcement Learning Workshop at NeurIPS (2018-2022)
• Guest lecturer: RL part of 先端人工知能論II at the University of Tokyo (2016-2018)

Education

• M.S. Information Science and Technology (April 2013 - March 2015)

  • Graduate School of Information Science and Technology, The University of Tokyo
  • Thesis: “Automatic Feature Generation and Model Learning for General Game Players Based on Reinforcement Learning“

• B.S Engineering (April 2011 - March 2013)