Bio

PhD (Statistical Sciences) from the Institute of Statistical Mathematics with a doctoral thesis entitled “Identification of Importance-Weighting and Geodesics on Statistical Manifolds”.

Selected publications

Information geometry and machine learning

  1. Masanari Kimura and Hideitsu Hino. “Information Geometrically Generalized Covariate Shift Adaptation.” Neural Computation 34.9 (2022): 1944-1977. paper
  2. Masanari Kimura and Hideitsu Hino. “α-geodesical skew divergence.” Entropy 23.5 (2021): 528. paper
  3. Masanari Kimura. “Generalized t-SNE Through the Lens of Information Geometry.” IEEE Access 9 (2021): 129619-129625. paper

Theoretical analysis of data augmentation

  1. Masanari Kimura. “Generalization Bounds for Set-to-Set Matching with Negative Sampling.” International Conference on Neural Information Processing (ICONIP 2022). paper
  2. Masanari Kimura. “Understanding test-time augmentation.” International Conference on Neural Information Processing (ICONIP 2021). paper
  3. Masanari Kimura. “Why mixup improves the model performance.” International Conference on Artificial Neural Networks (ICANN 2021). paper

Distribution shift and active learning

  1. Masanari Kimura and Hideitsu Hino. “A Short Survey on Importance Weighting for Machine Learning”. Transactions on Machine Learning Research. (TMLR 2024). paper
  2. Masanari Kimura, Takuma Nakamura and Yuki Saito. “SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts.” IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop (CVPRW 2023). paper
  3. Masanari Kimura, Kei Wakabayashi, and Atsuyuki Morishima. “Batch prioritization of data labeling tasks for training classifiers.” AAAI Conference on Human Computation and Crowdsourcing (AAAI HCOMP 2020). paper

Publication list

Google Scholar