First Authored

  • Miyaguchi, K. (2022). A Theoretical Framework of Almost Hyperparameter-free Hyperparameter Selection Methods for Offline Policy Evaluation. AAAI-22 Workshop.
  • Miyaguchi, K., Katsuki, T., Koseki, A., & Iwamori, T. (2022). Variational Inference for Discriminative Learning with Generative Modeling of Feature Incompletion. ICLR-22 (Oral).
  • Miyaguchi, K. (2021). Asymptotically Exact Error Characterization of Offline Policy Evaluation with Linear Direct Method under Unrealizability. NeurIPS-21.
  • Miyaguchi, K. (2019). PAC-Bayesian transportation bound. arXiv preprint arXiv:1905.13435.
  • Miyaguchi, K. & Yamanishi, K. (2019). Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional l1-Balls via Envelope Complexity. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS’19), 89, 3440-3448, Okinawa, Japan.
  • Miyaguchi, K. & Kajino, K. (2019). Cogra: Concept-drift-aware Stochastic Gradient Descent for Time-series Forecasting, In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Hawaii, USA.
  • Miyaguchi, K., & Yamanishi, K. (2018). High-dimensional penalty selection via minimum description length principle. Machine Learning, 107(8-10), 1283-1302.
  • Miyaguchi, K., Matsushima, S., & Yamanishi, K. (2017). Sparse Graphical Modeling via Stochastic Complexity. In Proceedings of 2017 SIAM International Conference on Data Mining, 723-731, Texas, USA.
  • Miyaguchi, K., Matsushima, S., & Yamanishi, K. (2016). Stochastic Complexity for Sparse Modeling. In Proceedings of Ninth Workshop on Information Theoretic Methods in Science and Engineering, 24-25, Helsinki, Finland.
  • Miyaguchi, K., & Yamanishi, K. (2015). On-line detection of continuous changes in stochastic processes. In Proceedings of IEEE International Conference on Data Science and Advanced Analytics, 1-9.

Collaboration

  • Kajino, H., Miyaguchi, K., & Osogami, T. (2022). Biases in In Silico Evaluation of Molecular Optimization Methods and Bias-Reduced Evaluation Methodology. arXiv preprint arXiv:2201.12163.
  • Katsuki, T., Miyaguchi, K., Koseki, A., Iwamori, T., Yanagiya, R., & Suzuki, A. (2022). Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction. IJCAI-22.
  • Kaneko, R., Miyaguchi, K., & Yamanishi, K. (2017). Detecting changes in streaming data with information-theoretic windowing. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 646-655). IEEE.
  • Suzuki, A., Miyaguchi, K., & Yamanishi, K. (2016). Structure selection for convolutive non-negative matrix factorization using normalized maximum likelihood coding. In 2016 IEEE 16th International Conference on Data Mining (ICDM) (pp. 1221-1226). IEEE.
  • Yamanishi, K., & Miyaguchi, K. (2016). Detecting gradual changes from data stream using MDL-change statistics. In 2016 IEEE International Conference on Big Data (Big Data) (pp. 156-163). IEEE.

Talks

  • Miyaguchi, K. (2022). 汎化誤差解析から始める統計的学習理論 (Introduction to Statistical Learning Theory with Generalization Error Analysis). IBIS Workshop 2022 tutorial. slide