| Detecting gradual changes from data stream using MDL-change statistics K Yamanishi, K Miyaguchi 2016 IEEE International Conference on Big Data (Big Data), 156-163, 2016 | 26 | 2016 |
| Online detection of continuous changes in stochastic processes K Miyaguchi, K Yamanishi International Journal of Data Science and Analytics 3, 213-229, 2017 | 16 | 2017 |
| Cogra: Concept-drift-aware stochastic gradient descent for time-series forecasting K Miyaguchi, H Kajino Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4594-4601, 2019 | 11 | 2019 |
| Detecting changes in streaming data with information-theoretic windowing R Kaneko, K Miyaguchi, K Yamanishi 2017 IEEE International Conference on Big Data (Big Data), 646-655, 2017 | 10 | 2017 |
| Study on learning from nonstationary time series (Unpublished master's thesis) K Miyaguchi University of Tokyo, Tokyo, 2016 | 9 | 2016 |
| PAC-Bayesian transportation bound K Miyaguchi arXiv preprint arXiv:1905.13435, 2019 | 6 | 2019 |
| Adaptive minimax regret against smooth logarithmic losses over high-dimensional l1-balls via envelope complexity K Miyaguchi, K Yamanishi The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 6 | 2019 |
| High-dimensional penalty selection via minimum description length principle K Miyaguchi, K Yamanishi Machine Learning 107, 1283-1302, 2018 | 6 | 2018 |
| Normalized maximum likelihood with luckiness for multivariate normal distributions K Miyaguchi arXiv preprint arXiv:1708.01861, 2017 | 4 | 2017 |
| Structure selection for convolutive non-negative matrix factorization using normalized maximum likelihood coding A Suzuki, K Miyaguchi, K Yamanishi 2016 IEEE 16th International Conference on Data Mining (ICDM), 1221-1226, 2016 | 4 | 2016 |
| Hierarchical lattice layer for partially monotone neural networks H Yanagisawa, K Miyaguchi, T Katsuki Advances in Neural Information Processing Systems 35, 11092-11103, 2022 | 3 | 2022 |
| Asymptotically exact error characterization of offline policy evaluation with misspecified linear models K Miyaguchi Advances in Neural Information Processing Systems 34, 28573-28584, 2021 | 3 | 2021 |
| Sparse graphical modeling via stochastic complexity K Miyaguchi, S Matsushima, K Yamanishi Proceedings of the 2017 SIAM International Conference on Data Mining, 723-731, 2017 | 3 | 2017 |
| A theoretical framework of almost hyperparameter-free hyperparameter selection methods for offline policy evaluation K Miyaguchi arXiv preprint arXiv:2201.02300, 2022 | 2 | 2022 |
| Data pruning in tree-based fitted q iteration T Osogami, R Iwaki, K Miyaguchi US Patent App. 17/192,308, 2022 | 1 | 2022 |
| Divide-and-conquer framework for quantile regression H Yanagisawa, K Miyaguchi, T Katsuki US Patent App. 17/093,804, 2022 | 1 | 2022 |
| Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction T Katsuki, K Miyaguchi, A Koseki, T Iwamori, R Yanagiya, A Suzuki arXiv preprint arXiv:2204.13451, 2022 | 1 | 2022 |
| Almost Hyperparameter-Free Hyperparameter Selection Framework for Offline Policy Evaluation K Miyaguchi AAAI Conference on Artificial Intelligence, 2022 | 1 | 2022 |
| Biases in In Silico Evaluation of Molecular Optimization Methods and Bias-Reduced Evaluation Methodology H Kajino, K Miyaguchi, T Osogami arXiv preprint arXiv:2201.12163, 2022 | 1 | 2022 |
| Hyperparameter Selection Methods for Fitted Q-Evaluation with Error Guarantee K Miyaguchi arXiv preprint arXiv:2201.02300, 2022 | 1 | 2022 |
