| Multiple graph label propagation by sparse integration M Karasuyama, H Mamitsuka IEEE transactions on neural networks and learning systems 24 (12), 1999-2012, 2013 | 149 | 2013 |
| Multiple incremental decremental learning of support vector machines M Karasuyama, I Takeuchi IEEE Transactions on Neural Networks 21 (7), 1048-1059, 2010 | 147 | 2010 |
| 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 | 135 | 2020 |
| Manifold-based similarity adaptation for label propagation M Karasuyama, H Mamitsuka Advances in neural information processing systems 26, 2013 | 84 | 2013 |
| 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 | 82 | 2020 |
| 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 | 75 | 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 | 60 | 2018 |
| Multiple incremental decremental learning of support vector machines M Karasuyama, I Takeuchi Advances in neural information processing systems 22, 2009 | 59 | 2009 |
| 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 | 58 | 2020 |
| 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 | 57 | 2016 |
| 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 | 49 | 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 | 41 | 2012 |
| Adaptive edge weighting for graph-based learning algorithms M Karasuyama, H Mamitsuka Machine Learning 106 (2), 307-335, 2017 | 37 | 2017 |
| 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 | 34 | 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 | 33 | 2017 |
| Canonical dependency analysis based on squared-loss mutual information M Karasuyama, M Sugiyama Neural Networks 34, 46-55, 2012 | 32 | 2012 |
| Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design K Inoue, M Karasuyama, R Nakamura, M Konno, D Yamada, K Mannen, ... Communications Biology 4 (1), 1-11, 2021 | 27 | 2021 |
| Efficient Experimental Search for Discovering a Fast Li-Ion Conductor from a Perovskite-Type LixLa(1–x)/3NbO3 (LLNO) Solid-State Electrolyte Using Bayesian … Z Yang, S Suzuki, N Tanibata, H Takeda, M Nakayama, M Karasuyama, ... The Journal of Physical Chemistry C 125 (1), 152-160, 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 | 24 | 2017 |
| Computational design of stable and highly ion-conductive materials using multi-objective bayesian optimization: Case studies on diffusion of oxygen and lithium M Karasuyama, H Kasugai, T Tamura, K Shitara Computational Materials Science 184, 109927, 2020 | 22 | 2020 |
