-
Natural language processing
-
Safe AI - Uncertainty in machine learning, Outlier detection
-
Deep generative models
Education
-
The University of Texas at Austin, U.S. - M.S. in CS
2020-2022
-
The University of Tokyo, Japan
2020-2020
-
Department of Computer Science, Graduate School of Information Science and Technology
-
Akiko Aizawa Lab
-
Keio University, Japan - B.E. in Statistics
2016-2020
-
Department of Mathematics
-
Kei Kobayashi Lab
-
Carnegie Mellon University, Pennsylvania - Exchange student
2018-2019
-
Department of Statistics and Data Science
Work Experience
-
SenseTime Japan, Japan - Research Internship
2020
-
Datasection Inc, Japan - Research Internship
2017-2018
-
Research in natural language processing
Publications
Google Scholar
-
Ryo Kamoi, Kei Kobayashi. Why is the Mahalanobis Distance Effective for Anomaly Detection?. arXiv preprint arXiv:2003.00402. 2020.
[pdf]
-
Ryo Kamoi, Kei Kobayashi. Out-of-Distribution Detection with Likelihoods Assigned by Deep Generative Models Using Multimodal Prior Distributions. AAAI Workshop on Artificial Intelligence Safety. 2020.
[pdf]
-
Ryo Kamoi, Kei Kobayashi. Likelihood Assignment for Out-of-Distribution Inputs in Deep Generative Models is Sensitive to Prior Distribution Choice. arXiv preprint arXiv:1911.06515. 2019.
[pdf]
Awards, Scholarships