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Masayuki Karasuyama
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2020 – today
- 2024
- [c27]Ryota Ozaki, Kazuki Ishikawa, Youhei Kanzaki, Shion Takeno, Ichiro Takeuchi, Masayuki Karasuyama:
Multi-Objective Bayesian Optimization with Active Preference Learning. AAAI 2024: 14490-14498 - [c26]Shion Takeno
, Masahiro Nomura
Hot off the Press: Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes. GECCO Companion 2024: 59-60 - [c25]Shion Takeno, Yu Inatsu, Masayuki Karasuyama, Ichiro Takeuchi:
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds. ICML 2024 - [c24]Tajima Shinji
Learning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attribute. KDD 2024: 2830-2841 - [i18]Tajima Shinji, Ren Sugihara, Ryota Kitahara, Masayuki Karasuyama:
Learning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attribute. CoRR abs/2402.06932 (2024) - [i17]Shion Takeno, Yu Inatsu, Masayuki Karasuyama:
Regret Analysis for Randomized Gaussian Process Upper Confidence Bound. CoRR abs/2409.00979 (2024) - 2023
- [c23]Hideaki Ishibashi, Masayuki Karasuyama, Ichiro Takeuchi, Hideitsu Hino:
A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets. AISTATS 2023: 6463-6497 - [c22]Shion Takeno, Yu Inatsu, Masayuki Karasuyama:
Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds. ICML 2023: 33490-33515 - [c21]Shion Takeno, Masahiro Nomura, Masayuki Karasuyama:
Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes. ICML 2023: 33516-33533 - [i16]Shion Takeno, Yu Inatsu, Masayuki Karasuyama:
Randomized Gaussian Process Upper Confidence Bound with Tight Bayesian Regret Bounds. CoRR abs/2302.01511 (2023) - [i15]Shion Takeno, Masahiro Nomura, Masayuki Karasuyama:
Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes. CoRR abs/2302.01513 (2023) - [i14]Shion Takeno, Yu Inatsu, Masayuki Karasuyama, Ichiro Takeuchi:
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds. CoRR abs/2311.03760 (2023) - [i13]Ryota Ozaki, Kazuki Ishikawa, Youhei Kanzaki, Shinya Suzuki, Shion Takeno, Ichiro Takeuchi, Masayuki Karasuyama:
Multi-Objective Bayesian Optimization with Active Preference Learning. CoRR abs/2311.13460 (2023) - 2022
- [j18]Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada
A Generalized Framework of Multifidelity Max-Value Entropy Search Through Joint Entropy. Neural Comput. 34(10): 2145-2203 (2022) - [j17]Shunya Kusakawa, Shion Takeno, Yu Inatsu, Kentaro Kutsukake, Shogo Iwazaki, Takashi Nakano, Toru Ujihara, Masayuki Karasuyama, Ichiro Takeuchi:
Bayesian Optimization for Cascade-Type Multistage Processes. Neural Comput. 34(12): 2408-2431 (2022) - [j16]Vo Nguyen Le Duy
Stat-DSM: Statistically Discriminative Sub-Trajectory Mining With Multiple Testing Correction. IEEE Trans. Knowl. Data Eng. 34(3): 1477-1488 (2022) - [c20]Yu Inatsu, Shion Takeno, Masayuki Karasuyama, Ichiro Takeuchi:
Bayesian Optimization for Distributionally Robust Chance-constrained Problem. ICML 2022: 9602-9621 - [c19]Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, Masayuki Karasuyama:
Sequential and Parallel Constrained Max-value Entropy Search via Information Lower Bound. ICML 2022: 20960-20986 - [i12]Yu Inatsu, Shion Takeno, Masayuki Karasuyama, Ichiro Takeuchi:
Bayesian Optimization for Distributionally Robust Chance-constrained Problem. CoRR abs/2201.13112 (2022) - 2021
- [j15]Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama
Distance metric learning for graph structured data. Mach. Learn. 110(7): 1765-1811 (2021) - [i11]Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, Masayuki Karasuyama:
Sequential- and Parallel- Constrained Max-value Entropy Search via Information Lower Bound. CoRR abs/2102.09788 (2021) - [i10]Shunya Kusakawa, Shion Takeno, Yu Inatsu, Kentaro Kutsukake, Shogo Iwazaki, Takashi Nakano, Toru Ujihara, Masayuki Karasuyama, Ichiro Takeuchi:
Bayesian Optimization for Cascade-type Multi-stage Processes. CoRR abs/2111.08330 (2021) - 2020
- [j14]Yu Inatsu, Masayuki Karasuyama, Keiichi Inoue, Hideki Kandori, Ichiro Takeuchi:
Active Learning of Bayesian Linear Models with High-Dimensional Binary Features by Parameter Confidence-Region Estimation. Neural Comput. 32(10): 1998-2031 (2020) - [j13]Yu Inatsu, Masayuki Karasuyama, Keiichi Inoue, Ichiro Takeuchi:
Active Learning for Level Set Estimation Under Input Uncertainty and Its Extensions. Neural Comput. 32(12): 2486-2531 (2020) - [c18]Shinya Suzuki, Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, Masayuki Karasuyama:
Multi-objective Bayesian Optimization using Pareto-frontier Entropy. ICML 2020: 9279-9288 - [c17]Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama:
Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization. ICML 2020: 9334-9345 - [i9]Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama:
Distance Metric Learning for Graph Structured Data. CoRR abs/2002.00727 (2020) - [i8]Shion Takeno, Yuhki Tsukada, Hitoshi Fukuoka, Toshiyuki Koyama, Motoki Shiga, Masayuki Karasuyama:
Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling. CoRR abs/2003.13428 (2020)
2010 – 2019
- 2019
- [j12]Takuto Sakuma, Kazuya Nishi, Kaoru Kishimoto, Kazuya Nakagawa, Masayuki Karasuyama, Yuta Umezu, Shinsuke Kajioka, Shuhei J. Yamazaki, Koutarou D. Kimura
Efficient learning algorithm for sparse subsequence pattern-based classification and applications to comparative animal trajectory data analysis. Adv. Robotics 33(3-4): 134-152 (2019) - [j11]Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama:
Safe Triplet Screening for Distance Metric Learning. Neural Comput. 31(12): 2432-2491 (2019) - [c16]Vo Nguyen Le Duy, Takuto Sakuma, Taiju Ishiyama, Hiroki Toda, Kazuya Arai, Masayuki Karasuyama, Yuta Okubo, Masayuki Sunaga, Yasuo Tabei, Ichiro Takeuchi:
Statistically Discriminative Sub-trajectory Mining with Multiple Testing Correction. SIGSPATIAL/GIS 2019: 548-551 - [c15]Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama:
Learning Interpretable Metric between Graphs: Convex Formulation and Computation with Graph Mining. KDD 2019: 1026-1036 - [i7]Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama:
Multi-fidelity Bayesian Optimization with Max-value Entropy Search. CoRR abs/1901.08275 (2019) - [i6]Vo Nguyen Le Duy, Takuto Sakuma, Taiju Ishiyama, Hiroki Toda, Kazuya Nishi, Masayuki Karasuyama, Yuta Okubo, Masayuki Sunaga, Yasuo Tabei, Ichiro Takeuchi:
Statistically Discriminative Sub-trajectory Mining. CoRR abs/1905.01788 (2019) - [i5]Shinya Suzuki, Shion Takeno, Tomoyuki Tamura, Kazuki Shitara, Masayuki Karasuyama:
Multi-objective Bayesian Optimization using Pareto-frontier Entropy. CoRR abs/1906.00127 (2019) - [i4]Yu Inatsu, Masayuki Karasuyama, Keiichi Inoue, Ichiro Takeuchi:
Active learning for level set estimation under cost-dependent input uncertainty. CoRR abs/1909.06064 (2019) - 2018
- [c14]Masayuki Karasuyama, Hiroshi Mamitsuka:
Factor Analysis on a Graph. AISTATS 2018: 1117-1126 - [c13]Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama:
Safe Triplet Screening for Distance Metric Learning. KDD 2018: 2653-2662 - 2017
- [j10]Sohiya Yotsukura
Exploring phenotype patterns of breast cancer within somatic mutations: a modicum in the intrinsic code. Briefings Bioinform. 18(4): 619-633 (2017) - [j9]Masayuki Karasuyama, Hiroshi Mamitsuka
Adaptive edge weighting for graph-based learning algorithms. Mach. Learn. 106(2): 307-335 (2017) - [j8]Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama
Homotopy continuation approaches for robust SV classification and regression. Mach. Learn. 106(7): 1009-1038 (2017) - 2016
- [c12]Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano, Ichiro Takeuchi:
Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling. ICML 2016: 1577-1586 - [c11]Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama, Koji Tsuda, Ichiro Takeuchi:
Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining. KDD 2016: 1785-1794 - 2015
- [c10]Atsushi Shibagaki, Yoshiki Suzuki, Masayuki Karasuyama, Ichiro Takeuchi:
Regularization Path of Cross-Validation Error Lower Bounds. NIPS 2015: 1675-1683 - [i3]Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Masayuki Karasuyama, Ichiro Takeuchi:
Homotopy Continuation Approaches for Robust SV Classification and Regression. CoRR abs/1507.03229 (2015) - 2013
- [j7]Masayuki Karasuyama, Hiroshi Mamitsuka
Multiple Graph Label Propagation by Sparse Integration. IEEE Trans. Neural Networks Learn. Syst. 24(12): 1999-2012 (2013) - [c9]Masayuki Karasuyama, Hiroshi Mamitsuka:
Manifold-based Similarity Adaptation for Label Propagation. NIPS 2013: 1547-1555 - 2012
- [j6]Masayuki Karasuyama, Naoyuki Harada, Masashi Sugiyama
Multi-parametric solution-path algorithm for instance-weighted support vector machines. Mach. Learn. 88(3): 297-330 (2012) - [j5]Masayuki Karasuyama, Masashi Sugiyama
Canonical dependency analysis based on squared-loss mutual information. Neural Networks 34: 46-55 (2012) - 2011
- [j4]Masayuki Karasuyama, Ichiro Takeuchi:
Nonlinear Regularization Path for Quadratic Loss Support Vector Machines. IEEE Trans. Neural Networks 22(10): 1613-1625 (2011) - [c8]Masayuki Karasuyama, Ichiro Takeuchi:
Suboptimal Solution Path Algorithm for Support Vector Machine. ICML 2011: 473-480 - [c7]Masayuki Karasuyama, Naoyuki Harada, Masashi Sugiyama
Multi-parametric solution-path algorithm for instance-weighted support vector machines. MLSP 2011: 1-6 - [i2]Masayuki Karasuyama, Ichiro Takeuchi:
Suboptimal Solution Path Algorithm for Support Vector Machine. CoRR abs/1105.0471 (2011) - 2010
- [j3]Masayuki Karasuyama, Ichiro Takeuchi:
Multiple incremental decremental learning of support vector machines. IEEE Trans. Neural Networks 21(7): 1048-1059 (2010) - [c6]Masayuki Karasuyama, Ichiro Takeuchi:
Nonlinear regularization path for the modified Huber loss Support Vector Machines. IJCNN 2010: 1-8 - [i1]Masayuki Karasuyama, Naoyuki Harada, Masashi Sugiyama, Ichiro Takeuchi:
Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines. CoRR abs/1009.4791 (2010)
2000 – 2009
- 2009
- [j2]Hiroyuki Moriguchi, Ichiro Takeuchi, Masayuki Karasuyama, Shin-ichi Horikawa, Yoshikatsu Ohta, Tetsuji Kodama, Hiroshi Naruse:
Adaptive Kernel Quantile Regression for Anomaly Detection. J. Adv. Comput. Intell. Intell. Informatics 13(3): 230-236 (2009) - [j1]Masayuki Karasuyama, Ichiro Takeuchi, Ryohei Nakano:
Efficient Leave-m-out Cross-Validation of Support Vector Regression by Generalizing Decremental Algorithm. New Gener. Comput. 27(4): 307-318 (2009) - [c5]Masayuki Karasuyama, Ichiro Takeuchi:
Multiple Incremental Decremental Learning of Support Vector Machines. NIPS 2009: 907-915 - 2008
- [c4]Masayuki Karasuyama, Ryohei Nakano:
Optimizing Sparse Kernel Ridge Regression hyperparameters based on leave-one-out cross-validation. IJCNN 2008: 3463-3468 - [c3]Masayuki Karasuyama, Ichiro Takeuchi, Ryohei Nakano:
Reducing SVR Support Vectors by Using Backward Deletion. KES (3) 2008: 76-83 - 2007
- [c2]Masayuki Karasuyama, Ryohei Nakano:
Optimizing SVR Hyperparameters via Fast Cross-Validation using AOSVR. IJCNN 2007: 1186-1191 - 2006
- [c1]Masayuki Karasuyama, Daisuke Kitakoshi, Ryohei Nakano:
Revised Optimizer of SVR Hyperparameters Minimizing Cross-Validation Error. IJCNN 2006: 319-326
Coauthor Index
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