takashinagata.com

Takashi NAGATA

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my linkedin profile Email: takashin at uci dot edu
    Takashi NAGATA is a scientist working on visual search problems. He received his Ph.D. degree in Computer Science from University of California, Irvine in 2022. Before obtaining the Ph.D. degree, he worked 7.5 years in total as a systems engineer in financial industry and a technical support engineer for BigData technologies after he received B.S. degree in information science from Tokyo University of Science, Japan, in 2008 and M.S. in 2010 respectively. 

Research Interests

I’m interested in the intersection of machine learning and cognitive science, and its application such as robotics.

Work Experience

Education

Publications

  • Xing, J., Nagata, T., Zou, X., Neftci, E., Krichmar, J.L. (2023). Achieving efficient interpretability of reinforcement learning via policy distillation and selective input gradient regularization. Neural Networks 161, 228-241.
  • Nagata, T., Xing, J., Kumazawa, T., & Neftci, E. (2022). Uncertainty Aware Model Integration on Reinforcement Learning. IJCNN'22.
  • Xing, J., Nagata, T., Zou, X., Neftci, E., & Krichmar, J.L. (2022). Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability. (Preprint)
  • Xing, J., Nagata, T., Chen, K., Zou, X., Neftci, E., & Krichmar, J.L. (2021). Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation. AAAI'21.
  • Nagata, T., Takimoto, M., & Kambayashi, Y. (2013). Cooperatively Searching Objects Based on Mobile Agents. Trans. Comput. Collect. Intell., 11, 119-136.
  • Nagata, T., Takimoto, M., & Kambayashi, Y. (2009). Suppressing the Total Costs of Executing Tasks Using Mobile Agents. 2009 42nd Hawaii International Conference on System Sciences, 1-10.

Skills