Masayuki Karasuyama

Masayuki Karasuyama, Ryohei Nakano, R. Nakano, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Computational design of stable and highly ion-conductive materials using multi-objective bayesian optimization: Case studies on diffusion of oxygen and lithium

Masayuki Karasuyama, Tomoyuki Tamura, Kazuki Shitara, 2020, Computational Materials Science.

Optimizing SVR Hyperparameters via Fast Cross-Validation using AOSVR

Masayuki Karasuyama, Ryohei Nakano, 2007, 2007 International Joint Conference on Neural Networks.

Suboptimal Solution Path Algorithm for Support Vector Machine pdf

Ichiro Takeuchi, Masayuki Karasuyama, I. Takeuchi, 2011, ICML.

Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining

Ichiro Takeuchi, Masayuki Karasuyama, Shinya Suzumura, 2016, KDD.

Exploring phenotype patterns of breast cancer within somatic mutations: a modicum in the intrinsic code

Masayuki Karasuyama, Ichigaku Takigawa, Hiroshi Mamitsuka, 2016, Briefings Bioinform..

Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling pdf

Masayuki Karasuyama, Yuhki Tsukada, Shion Takeno, 2020, Physical Review Materials.

Multi-parametric solution-path algorithm for instance-weighted support vector machines

Masashi Sugiyama, Ichiro Takeuchi, Masayuki Karasuyama, 2011, 2011 IEEE International Workshop on Machine Learning for Signal Processing.

Efficient Leave-m-out Cross-Validation of Support Vector Regression by Generalizing Decremental Algorithm

Ichiro Takeuchi, Masayuki Karasuyama, Ryohei Nakano, 2009, New Generation Computing.

Safe Feature Pruning for Sparse High-Order Interaction Models

Ichiro Takeuchi, Masayuki Karasuyama, Shinya Suzumura, 2015, 1506.08002.

Revised Optimizer of SVR Hyperparameters Minimizing Cross-Validation Error

Masayuki Karasuyama, Ryohei Nakano, Daisuke Kitakoshi, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

F-017 An Efficient Algorithm for SVM-based Multivariate Two-sample Test Using Path-Following Approach and Its Application to Gene Set

Ichiro Takeuchi, Masayuki Karasuyama, Taisuke Izumi, 2011 .

Multi-parametric solution-path algorithm for instance-weighted support vector machines

Masashi Sugiyama, Ichiro Takeuchi, Masayuki Karasuyama, 2011, MLSP.

Nonlinear regularization path for the modified Huber loss Support Vector Machines

Ichiro Takeuchi, Masayuki Karasuyama, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Active Learning for Level Set Estimation Under Input Uncertainty and Its Extensions

Ichiro Takeuchi, Masayuki Karasuyama, Keiichi Inoue, 2020, Neural Computation.

Statistically Discriminative Sub-trajectory Mining with Multiple Testing Correction

Yasuo Tabei, Ichiro Takeuchi, Masayuki Karasuyama, 2019, SIGSPATIAL/GIS.

Bayesian Optimization for Cascade-type Multi-stage Processes pdf

Ichiro Takeuchi, Masayuki Karasuyama, Toru Ujihara, 2021, ArXiv.

Distance metric learning for graph structured data

Ichiro Takeuchi, Masayuki Karasuyama, Tomoki Yoshida, 2020, Machine Learning.

Sequential- and Parallel- Constrained Max-value Entropy Search via Information Lower Bound pdf

Masayuki Karasuyama, Tomoyuki Tamura, Shion Takeno, 2021, ArXiv.

Knowledge-transfer-based cost-effective search for interface structures: A case study on fcc-Al [110] tilt grain boundary

Masayuki Karasuyama, Ichiro Takeuchi, Tomoyuki Tamura, 2018, Physical Review Materials.

Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach

Ichiro Takeuchi, Masayuki Karasuyama, Hideki Kandori, 2018, Scientific Reports.

Fast and scalable prediction of local energy at grain boundaries: machine-learning based modeling of first-principles calculations

Masayuki Karasuyama, Ryo Kobayashi, Yoshinori Shiihara, 2017 .

Estimation of material parameters based on precipitate shape: efficient identification of low-error region with Gaussian process modeling

Masayuki Karasuyama, Yuhki Tsukada, Toshiyuki Koyama, 2019, Scientific Reports.

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

Masayuki Karasuyama, Atsuto Seko, Ichiro Takeuchi, 2015, 1512.00623.