Multiple graph label propagation by sparse integration M Karasuyama, H Mamitsuka IEEE transactions on neural networks and learning systems 24 (12), 1999-2012, 2013 | 125 | 2013 |

Multiple incremental decremental learning of support vector machines M Karasuyama, I Takeuchi IEEE Transactions on Neural Networks 21 (7), 1048-1059, 2010 | 112 | 2010 |

Manifold-based similarity adaptation for label propagation M Karasuyama, H Mamitsuka Advances in neural information processing systems 26, 2013 | 66 | 2013 |

Machine-learning-based selective sampling procedure for identifying the low-energy region in a potential energy surface: A case study on proton conduction in oxides K Toyoura, D Hirano, A Seko, M Shiga, A Kuwabara, M Karasuyama, ... Physical Review B 93 (5), 054112, 2016 | 62 | 2016 |

Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization S Takeno, H Fukuoka, Y Tsukada, T Koyama, M Shiga, I Takeuchi, ... International Conference on Machine Learning, 9334-9345, 2020 | 59 | 2020 |

Multiple incremental decremental learning of support vector machines M Karasuyama, I Takeuchi Advances in neural information processing systems 22, 2009 | 42 | 2009 |

Simultaneous safe screening of features and samples in doubly sparse modeling A Shibagaki, M Karasuyama, K Hatano, I Takeuchi International Conference on Machine Learning, 1577-1586, 2016 | 41 | 2016 |

Multi-parametric solution-path algorithm for instance-weighted support vector machines M Karasuyama, N Harada, M Sugiyama, I Takeuchi Machine learning 88 (3), 297-330, 2012 | 40 | 2012 |

Safe pattern pruning: An efficient approach for predictive pattern mining K Nakagawa, S Suzumura, M Karasuyama, K Tsuda, I Takeuchi Proceedings of the 22nd acm sigkdd international conference on knowledge …, 2016 | 34 | 2016 |

Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach M Karasuyama, K Inoue, R Nakamura, H Kandori, I Takeuchi Scientific reports 8 (1), 15580, 2018 | 32 | 2018 |

Multi-objective Bayesian Optimization using Pareto-frontier Entropy S Suzuki, S Takeno, T Tamura, K Shitara, M Karasuyama International Conference on Machine Learning, 9279-9288, 2020 | 29 | 2020 |

Exploring a potential energy surface by machine learning for characterizing atomic transport K Kanamori, K Toyoura, J Honda, K Hattori, A Seko, M Karasuyama, ... Physical Review B 97 (12), 125124, 2018 | 28 | 2018 |

Fast and scalable prediction of local energy at grain boundaries: machine-learning based modeling of first-principles calculations T Tamura, M Karasuyama, R Kobayashi, R Arakawa, Y Shiihara, ... Modelling and Simulation in Materials Science and Engineering 25 (7), 075003, 2017 | 27 | 2017 |

Canonical dependency analysis based on squared-loss mutual information M Karasuyama, M Sugiyama Neural Networks 34, 46-55, 2012 | 27 | 2012 |

Adaptive edge weighting for graph-based learning algorithms M Karasuyama, H Mamitsuka Machine Learning 106 (2), 307-335, 2017 | 25 | 2017 |

Bayesian-optimization-guided experimental search of NASICON-type solid electrolytes for all-solid-state Li-ion batteries M Harada, H Takeda, S Suzuki, K Nakano, N Tanibata, M Nakayama, ... Journal of Materials Chemistry A 8 (30), 15103-15109, 2020 | 24 | 2020 |

A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines M Gönen, BA Weir, GS Cowley, F Vazquez, Y Guan, A Jaiswal, ... Cell systems 5 (5), 485-497. e3, 2017 | 19 | 2017 |

Nonlinear regularization path for quadratic loss support vector machines M Karasuyama, I Takeuchi IEEE transactions on neural networks 22 (10), 1613-1625, 2011 | 15 | 2011 |

Suboptimal solution path algorithm for support vector machine M Karasuyama, I Takeuchi arXiv preprint arXiv:1105.0471, 2011 | 15 | 2011 |

Suboptimal Solution Path Algorithm for Support Vector Machine IT Masayuki Karasuyama Proceedings of the 28th International Conference on Machine Learning, 473-480, 2011 | 15* | 2011 |