Revised Optimizer of SVR Hyperparameters Minimizing Cross-Validation Error. - Masayuki Karasuyama; Daisuke Kitakoshi; Ryohei Nakano (IJCNN, 2006)

Optimizing SVR Hyperparameters via Fast Cross-Validation using AOSVR. - Masayuki Karasuyama; Ryohei Nakano (IJCNN, 2007)

Optimizing Sparse Kernel Ridge Regression hyperparameters based on leave-one-out cross-validation. - Masayuki Karasuyama; Ryohei Nakano (IJCNN, 2008)

Reducing SVR Support Vectors by Using Backward Deletion. - Masayuki Karasuyama; Ichiro Takeuchi; Ryohei Nakano (KES (3), 2008)

Adaptive Kernel Quantile Regression for Anomaly Detection. - Hiroyuki Moriguchi; Ichiro Takeuchi; Masayuki Karasuyama; Shin-ichi Horikawa; Yoshikatsu Ohta; Tetsuji Kodama; Hiroshi Naruse (J. Adv. Comput. Intell. Intell. Informatics, 2009)

Efficient Leave-m-out Cross-Validation of Support Vector Regression by Generalizing Decremental Algorithm. - Masayuki Karasuyama; Ichiro Takeuchi; Ryohei Nakano (New Gener. Comput., 2009)

Nonlinear regularization path for the modified Huber loss Support Vector Machines. - Masayuki Karasuyama; Ichiro Takeuchi (IJCNN, 2010)

Multiple incremental decremental learning of support vector machines. - Masayuki Karasuyama; Ichiro Takeuchi (IEEE Trans. Neural Networks, 2010)

Suboptimal Solution Path Algorithm for Support Vector Machine. - Masayuki Karasuyama; Ichiro Takeuchi (ICML, 2011)

Nonlinear Regularization Path for Quadratic Loss Support Vector Machines. - Masayuki Karasuyama; Ichiro Takeuchi (IEEE Trans. Neural Networks, 2011)

Multi-parametric solution-path algorithm for instance-weighted support vector machines. - Masayuki Karasuyama; Naoyuki Harada; Masashi Sugiyama; Ichiro Takeuchi (Mach. Learn., 2012)

Canonical dependency analysis based on squared-loss mutual information. - Masayuki Karasuyama; Masashi Sugiyama (Neural Networks, 2012)

Manifold-based Similarity Adaptation for Label Propagation. - Masayuki Karasuyama; Hiroshi Mamitsuka (NIPS, 2013)

Multiple Graph Label Propagation by Sparse Integration. - Masayuki Karasuyama; Hiroshi Mamitsuka (IEEE Trans. Neural Networks Learn. Syst., 2013)

Regularization Path of Cross-Validation Error Lower Bounds. - Atsushi Shibagaki; Yoshiki Suzuki; Masayuki Karasuyama; Ichiro Takeuchi (NIPS, 2015)

Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling. - Atsushi Shibagaki; Masayuki Karasuyama; Kohei Hatano; Ichiro Takeuchi (ICML, 2016)

Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining. - Kazuya Nakagawa; Shinya Suzumura; Masayuki Karasuyama; Koji Tsuda; Ichiro Takeuchi (KDD, 2016)

Exploring phenotype patterns of breast cancer within somatic mutations: a modicum in the intrinsic code. - Sohiya Yotsukura; Masayuki Karasuyama; Ichigaku Takigawa; Hiroshi Mamitsuka (Briefings Bioinform., 2017)

Adaptive edge weighting for graph-based learning algorithms. - Masayuki Karasuyama; Hiroshi Mamitsuka (Mach. Learn., 2017)

Homotopy continuation approaches for robust SV classification and regression. - Shinya Suzumura; Kohei Ogawa; Masashi Sugiyama; Masayuki Karasuyama; Ichiro Takeuchi (Mach. Learn., 2017)

Factor Analysis on a Graph. - Masayuki Karasuyama; Hiroshi Mamitsuka (AISTATS, 2018)

Statistically Discriminative Sub-trajectory Mining with Multiple Testing Correction. - Vo Nguyen Le Duy; Takuto Sakuma; Taiju Ishiyama; Hiroki Toda; Kazuya Arai; Masayuki Karasuyama; Yuta Okubo; Masayuki Sunaga; Yasuo Tabei; Ichiro Takeuchi (SIGSPATIAL/GIS, 2019)

Learning Interpretable Metric between Graphs: Convex Formulation and Computation with Graph Mining. - Tomoki Yoshida; Ichiro Takeuchi; Masayuki Karasuyama (KDD, 2019)

Efficient learning algorithm for sparse subsequence pattern-based classification and applications to comparative animal trajectory data analysis. - Takuto Sakuma; Kazuya Nishi; Kaoru Kishimoto; Kazuya Nakagawa; Masayuki Karasuyama; Yuta Umezu; Shinsuke Kajioka; Shuhei J. Yamazaki; Koutarou D. Kimura; Sakiko Matsumoto; Ken Yoda; Matasaburo Fukutomi; et al. (Adv. Robotics, 2019)

Multi-fidelity Bayesian Optimization with Max-value Entropy Search. - Shion Takeno; Hitoshi Fukuoka; Yuhki Tsukada; Toshiyuki Koyama; Motoki Shiga; Ichiro Takeuchi; Masayuki Karasuyama (CoRR, 2019)

Statistically Discriminative Sub-trajectory Mining. - Vo Nguyen Le Duy; Takuto Sakuma; Taiju Ishiyama; Hiroki Toda; Kazuya Nishi; Masayuki Karasuyama; Yuta Okubo; Masayuki Sunaga; Yasuo Tabei; Ichiro Takeuchi (CoRR, 2019)

Active learning for level set estimation under cost-dependent input uncertainty. - Yu Inatsu; Masayuki Karasuyama; Keiichi Inoue; Ichiro Takeuchi (CoRR, 2019)

Safe Triplet Screening for Distance Metric Learning. - Tomoki Yoshida; Ichiro Takeuchi; Masayuki Karasuyama (Neural Comput., 2019)

Multi-objective Bayesian Optimization using Pareto-frontier Entropy. - Shinya Suzuki; Shion Takeno; Tomoyuki Tamura; Kazuki Shitara; Masayuki Karasuyama (ICML, 2020)

Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization. - Shion Takeno; Hitoshi Fukuoka; Yuhki Tsukada; Toshiyuki Koyama; Motoki Shiga; Ichiro Takeuchi; Masayuki Karasuyama (ICML, 2020)

Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling. - Shion Takeno; Yuhki Tsukada; Hitoshi Fukuoka; Toshiyuki Koyama; Motoki Shiga; Masayuki Karasuyama (CoRR, 2020)

Active Learning of Bayesian Linear Models with High-Dimensional Binary Features by Parameter Confidence-Region Estimation. - Yu Inatsu; Masayuki Karasuyama; Keiichi Inoue; Hideki Kandori; Ichiro Takeuchi (Neural Comput., 2020)

Active Learning for Level Set Estimation Under Input Uncertainty and Its Extensions. - Yu Inatsu; Masayuki Karasuyama; Keiichi Inoue; Ichiro Takeuchi (Neural Comput., 2020)

Bayesian Optimization for Cascade-type Multi-stage Processes. - Shunya Kusakawa; Shion Takeno; Yu Inatsu; Kentaro Kutsukake; Shogo Iwazaki; Takashi Nakano; Toru Ujihara; Masayuki Karasuyama; Ichiro Takeuchi (CoRR, 2021)

Distance metric learning for graph structured data. - Tomoki Yoshida; Ichiro Takeuchi; Masayuki Karasuyama (Mach. Learn., 2021)

Bayesian Optimization for Distributionally Robust Chance-constrained Problem. - Yu Inatsu; Shion Takeno; Masayuki Karasuyama; Ichiro Takeuchi (ICML, 2022)

Sequential and Parallel Constrained Max-value Entropy Search via Information Lower Bound. - Shion Takeno; Tomoyuki Tamura; Kazuki Shitara; Masayuki Karasuyama (ICML, 2022)

Bayesian Optimization for Cascade-Type Multistage Processes. - Shunya Kusakawa; Shion Takeno; Yu Inatsu; Kentaro Kutsukake; Shogo Iwazaki; Takashi Nakano; Toru Ujihara; Masayuki Karasuyama; Ichiro Takeuchi (Neural Comput., 2022)

A Generalized Framework of Multifidelity Max-Value Entropy Search Through Joint Entropy. - Shion Takeno; Hitoshi Fukuoka; Yuhki Tsukada; Toshiyuki Koyama; Motoki Shiga; Ichiro Takeuchi; Masayuki Karasuyama (Neural Comput., 2022)

Stat-DSM: Statistically Discriminative Sub-Trajectory Mining With Multiple Testing Correction. - Vo Nguyen Le Duy; Takuto Sakuma; Taiju Ishiyama; Hiroki Toda; Kazuya Arai; Masayuki Karasuyama; Yuta Okubo; Masayuki Sunaga; Hiroyuki Hanada; Yasuo Tabei; Ichiro Takeuchi (IEEE Trans. Knowl. Data Eng., 2022)

A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets. - Hideaki Ishibashi; Masayuki Karasuyama; Ichiro Takeuchi; Hideitsu Hino (AISTATS, 2023)

Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds. - Shion Takeno; Yu Inatsu; Masayuki Karasuyama (ICML, 2023)

Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes. - Shion Takeno; Masahiro Nomura; Masayuki Karasuyama (ICML, 2023)

Randomized Gaussian Process Upper Confidence Bound with Tight Bayesian Regret Bounds. - Shion Takeno; Yu Inatsu; Masayuki Karasuyama (CoRR, 2023)

Multi-Objective Bayesian Optimization with Active Preference Learning. - Ryota Ozaki 003; Kazuki Ishikawa 003; Youhei Kanzaki; Shinya Suzuki; Shion Takeno; Ichiro Takeuchi; Masayuki Karasuyama (CoRR, 2023)

Multi-Objective Bayesian Optimization with Active Preference Learning. - Ryota Ozaki 003; Kazuki Ishikawa 003; Youhei Kanzaki; Shion Takeno; Ichiro Takeuchi; Masayuki Karasuyama (AAAI, 2024)

Hot off the Press: Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes. - Shion Takeno; Masahiro Nomura; Masayuki Karasuyama (GECCO Companion, 2024)

Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds. - Shion Takeno; Yu Inatsu; Masayuki Karasuyama; Ichiro Takeuchi (ICML, 2024)

Learning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attribute. - Tajima Shinji; Ren Sugihara; Ryota Kitahara; Masayuki Karasuyama (KDD, 2024)

Regret Analysis for Randomized Gaussian Process Upper Confidence Bound. - Shion Takeno; Yu Inatsu; Masayuki Karasuyama (CoRR, 2024)

Pareto-frontier Entropy Search with Variational Lower Bound Maximization. - Masanori Ishikura; Masayuki Karasuyama (CoRR, 2025)

Bayesian Optimization for Simultaneous Selection of Machine Learning Algorithms and Hyperparameters on Shared Latent Space. - Kazuki Ishikawa 003; Ryota Ozaki 003; Yohei Kanzaki; Ichiro Takeuchi; Masayuki Karasuyama (CoRR, 2025)

Regret Analysis of Posterior Sampling-Based Expected Improvement for Bayesian Optimization. - Shion Takeno; Yu Inatsu; Masayuki Karasuyama; Ichiro Takeuchi (CoRR, 2025)