Japanese/English

KDD2019 at Anchorage, Alaska

Masayuki Karasuyama
Associate Professor
Department of Computer Science
Nagoya Institute of Technology
karasuyama[at mark]nitech.ac.jp

Research topics:
statistical machine learning, materials informatics, bioinformatics

Publication

Biography

---

Publication

Journal Paper (Refereed)

Back↑

• H. Fukuda, S. Kusakawa, K. Nakano, N. Tanibata, H. Takeda, M. Nakayama, M. Karasuyama, I. Takeuchi, T. Natorie and Y. Ono, Bayesian optimisation with transfer learning for NASICON-type solid electrolytes for all-solid-state Li-metal batteries, RSC Advances (IF in 2021 = 4.036), vol.12, no.47, pp.30696-30703, 2022.
• H. Hirai, T. Iizawa, T. Tamura, M. Karasuyama, R. Kobayashi, and T. Hirose, Machine-learning-based prediction of first-principles XANES spectra for amorphous materials, Physical Review Materials (IF in 2021 = 3.980), vol.6, no.16, 115601, 2022.
• S. Kusakawa, S. Takeno, Y. Inatsu, K. Kutsukake, S. Iwazaki, T. Nakano, T. Ujihara, M. Karasuyama, I. Takeuchi, Bayesian Optimization for Cascade-type Multistage Processes, Neural Computation (IF in 2021 = 3.278), vol.34, no.12, pp.2408-2431, 2022.
• M. Nakayama, K. Nakano, M. Harada, N. Tanibata, T. Hayami, Y. Noda, R. Kobayashi, M. Karasuyama, I. Takeuchi and M. Kotobuki, Na Superionic Conductor-Type LiZr2(PO4)3 as a Promising Solid Electrolyte for Use in All-Solid-State Li Metal Batteries, Chemical Communications (IF in 2021 = 6.605), vol.58, no.67, pp.9328-9340, 2022.
• S. Takeno, H. Fukuoka, Y. Tsukada, T. Koyama, M Shiga, I. Takeuchi and M. Karasuyama, A Generalized Framework of Multi-fidelity Max-value Entropy Search through Joint Entropy, Neural Computation (IF in 2021 = 3.278), vol.34, no.10, pp.2145-2203, 2022.
• T. Atsumi, K. Sato, Y. Yamaguchi, M. Hamaie, R. Yasuda, N. Tanibata, H. Takeda, M. Nakayama, M. Karasuyama and I. Takeuchi, Chemical composition data‐driven machine‐learning prediction for phase stability and materials properties of inorganic crystalline solids, physica status solidi (b) (IF in 2020 = 1.710), 259:2100525, 2022.
• T. Yoshida, I. Takeuchi, and M. Karasuyama, Distance Metric Learning for Graph Structured Data, Machine Learning (IF in 2019 = 2.672), vol.110, no.7, 1765-1811, 2021.
• K. Inoue*, M. Karasuyama* (* equal contribution), R. Nakamura, M Konno, D. Yamada, K. Mannen, T. Nagata, Y. Inatsu, H. Yawo, K. Yura, O. Béjà, H. Kandori, and I. Takeuchi, Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design, Communications Biology (IF in 2019 = 4.049) 4, 362, 2021.
• Z. Yang, S. Suzuki, N. Tanibata, H. Takeda, M. Nakayama, M. Karasuyama, and I. Takeuchi, Efficient Experimental Search for Discovering a Fast Li-Ion Conductor from a Perovskite-Type LixLa(1-x)/3NbO3 (LLNO) Solid-State Electrolyte Using Bayesian Optimization, Journal of Physical Chemistry C (IF in 2019 = 4.189), vol.125, no.1, pp.152-160, 2021.
• T. Tamura and M. Karasuyama, Prediction of formation energies of large-scale disordered systems via active-learning based executions of ab initio local-energy calculations: a case study on a Fe random grain boundary model with millions of atoms, Physical Review Materials (IF in 2018 = 2.926), vol.4, no.11, 113602, 2020.
• M. Harada, H. Takeda, S. Suzuki, K. Nakano, N. Tanibata, M. Nakayama, M. Karasuyama, and I. Takeuchi, Bayesian-optimization-guided experimental search of NASICON-type solid electrolytes for all-solid-state Li-ion batteries, Journal of Materials Chemistry A (IF in 2019 = 11.301), vol.8 no.30, pp.15103-15109, 2020.
• S. Takeno, Y. Tsukada, H. Fukuoka, T. Koyama, M. Shiga, and M. Karasuyama, Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling, Physical Review Materials (IF in 2018 = 2.926), vol.4, no.8, 083802, 2020.
• Y. Inatsu, M. Karasuyama, K. Inoue, and I. Takeuchi, Active Learning for Level Set Estimation Under Input Uncertainty and its Extensions, Neural Computation (IF in 2019 = 2.505), vol.32, no.12, pp.2486-2531, 2020.
• M. Karasuyama, H. Kasugai, T. Tamura, and K. Shitara, Computational Design of Stable and Highly Ion-conductive Materials using Multi-objective Bayesian Optimization: Case Studies on Diffusion of Oxygen and Lithium, Computational Materials Science (IF in 2019 = 2.863), vol.184, 109927, 2020.
• Y. Inatsu, M. Karasuyama, K. Inoue, H. Kandori, and I. Takeuchi, Active Learning of Bayesian Linear Models with High-Dimensional Binary Features by Parameter Confidence-Region Estimation, vol.32, no.10, pp.1998-2031, 2020. Neural Computation (IF in 2018 = 2.261), .
• V. N. L. Duy, T. Sakuma, T. Ishiyama, H. Toda, K. Arai, M. Karasuyama, Y. Okubo, M. Sunaga, H. Hanada, Y. Tabei, and I. Takeuchi, Stat-DSM: Statistically Discriminative Sub-trajectory Mining with Multiple Testing Correction, IEEE Transactions on Knowledge and Data Engineering (IF in 2018 = 3.857), vol. 34, no. 3, pp. 1477-1488, 2022.
• Y. Tsukada, S. Takeno, M. Karasuyama, H. Fukuoka, M. Shiga, and T. Koyama, Estimation of material parameters based on precipitate shape: efficient identification of low-error region with Gaussian process modeling, Scientific Reports (IF in 2018 = 4.525), vol.9, 15798, 2019.
• T. Yoshida, I. Takeuchi, and M. Karasuyama, Safe Triplet Screening for Distance Metric Learning, Neural Computation (IF in 2018 = 2.261), vol.31, no.12, pp.2432-2491, 2019.
• T. Sakuma, K. Nishi, K. Kishimoto, K. Nakagawa, M. Karasuyama, Y. Umezu, S. Kajioka, S. J. Yamazaki, K. D. Kimura, S. Matsumoto, K. Yoda, M. Fukutomi, H. Shidara, H. Ogawa and I. Takeuchi, Efficient learning algorithm for sparse subsequence pattern-based classification and applications to comparative animal trajectory data analysis, Advanced Robotics (IF in 2017 = 0.961), vol.33, no.3-4, pp.134-152, 2019.
• T. Yonezu, T. Tamura, I. Takeuchi, M. Karasuyama, Knowledge-Transfer based Cost-effective Search for Interface Structures: A Case Study on fcc-Al [110] Tilt Grain Boundary, Physical Review Materials (IF in 2018 = 2.926), vol.2 no.11, 113802, 2018.
• M. Karasuyama, K. Inoue, R. Nakamura, H. Kandori, and I. Takeuchi, Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach, Scientific Reports (IF in 2017 = 4.122), vol.8, no.1, 15580, 2018.
• K. Kanamori, K. Toyoura, J. Honda, K. Hattori, A. Seko, M. Karasuyama, K. Shitara, M. Shiga, A. Kuwabara, and I. Takeuchi, Exploring a potential energy surface by machine learning for characterizing atomic transport, Physical Review B (IF in 2016 = 3.836), vol.97, no.12, pp.125124, 2018.
• T. Tamura*, M. Karasuyama*, R. Kobayashi, R. Arakawa, Y. Shiihara, and I. Takeuchi, (* corresponding author), Fast and Scalable Prediction of Local Energy at Grain Boundaries: Machine-learning based Modeling of First-principles Calculations, Modelling and Simulation in Materials Science and Engineering (IF in 2016 = 1.891), vol.25, no.7, 075003, 2017.
• M. Gönen*, B. A Weir*, G. S Cowley*, Y. Guan*, A. Jaiswal*, M. Karasuyama*, V. Uzunangelov*, F. Vazquez*, T. Wang*, A. Airola, A. Bivol, J. Boehm, K. Bunte, D. Carlin, S. Chopra, A. Deran, K. Ellrott, P. Gopalacharyulu, K. Graim, B. Hoff, S. Howell, S. Kaski, S. A Khan, D. Marbach, Y. Newton, T. C Norman, S. Ng, T. Pahikkala, E. Paull, A. Sokolov, H. Tang, J. Tang, A. Tsherniak, K. Wennerberg, Y. Xie, X. Zhan, F. Zhu, Broad-DREAM Community, T. Aittokallio, H. Mamitsuka, D. Root, J. M Stuart, G. Xiao, G. Stolovitzky, W. C Hahn & A. A Margolin, (* equal contribution), A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines, Cell Systems (IF in 2016 = 8.406), vol.5, no.5, p485 497.e3, 2017.
• S. Suzumura, K. Ogawa, M. Sugiyama, M. Karasuyama, I. Takeuchi, Homotopy Continuation Approaches for Robust SV Classification and Regression, Machine Learning (IF in 2015 = 1.719), vol.106, no.7, pp.1009-1038, 2017.
• M. Karasuyama, and H. Mamitsuka, Adaptive Edge Weighting for Graph-Based Learning Algorithms, Machine Learning (IF in 2015 = 1.719), vol.106, no.2, pp.307-335, 2017. [Code]
• S. Yotsukura, M. Karasuyama, I. Takigawa, and H. Mamitsuka, Exploring Phenotype Patterns of Breast Cancer within Somatic Mutations: A Modicum in the Intrinsic Code, Briefings in Bioinformatics (IF in 2015 = 8.399), vol.18, no.4, pp.619 633, 2017.
• K. Toyoura, D. Hirano, A. Seko, M. Shiga, A. Kuwabara, M. Karasuyama, K. Shitara, and I. Takeuchi, A Machine Learning-based Selective Sampling Procedure for Identifying Low Energy Region in a Potential Energy Surface: A Case Study on Proton Conduction in Oxides, Physical Review B (IF in 2015 = 3.718), vol.93, no.5, pp.054112, 2016.
• M. Karasuyama and H. Mamitsuka, Multiple Graph Label Propagation by Sparse Integration, IEEE Transactions on Neural Networks and Learning Systems (IF in 2012 = 3.766), vol.24. no.12, pp.1999-2012, 2013. [Code]
• M. Karasuyama and M. Sugiyama, Canonical Dependency Analysis based on Squared-loss Mutual Information, Neural Networks (IF in 2011 = 2.182), vol.34, pp.46-55, 2012.
• M. Karasuyama, N. Harada, M. Sugiyama and I. Takeuchi, Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines, Machine Learning (IF in 2010 = 1.967), vol.88, no.3, pp.297-330, 2012.
• M. Karasuyama and I. Takeuchi, Nonlinear Regularization Path for Quadratic Loss Support Vector Machines, IEEE Transactions on Neural Networks (IF in 2010 = 2.633), vol.22, no.10, pp.1613-1625, Oct. 2011.
• M. Karasuyama and I. Takeuchi, Multiple Incremental Decremental Learning of Support Vector Machines, IEEE Transactions on Neural Networks (IF in 2010 = 2.633), vol.21, no.7, pp.1048-1059, July 2010.
• M. Karasuyama, I. Takeuchi and R. Nakano, Efficient Leave-m-out Cross-Validation of Support Vector Regression by Generalizing Decremental Algorithm, New Generation Computing (IF in 2009 = 0.364), vol. 27, no. 4, Special Issue on Data-Mining and Statistical Science, pp.307-318, 2009.
• H. Moriguchi, I. Takeuchi, M. Karasuyama, S. Horikawa, Y. Ohta, T. Kodama, and H. Naruse, Adaptive Kernel Quantile Regression for Anomaly Detection, Journal of Advanced Computational Intelligence and Inteligent Informatics, vol.13, no.3, pp.230-236, 2009.

International Conference and Workshop (Refereed)

Back↑

• H. Ishibashi, M. Karasuyama, I. Takeuchi, and H. Hino, A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets, Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023), to appear (Acceptance rate: 29%).
• S. Takeno, M. Nomura, and M. Karasuyama, Preferential Bayesian Optimization with Hallucination Believer, NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, 2022.
• S. Takeno, T. Tamura, K. Shitara, and M. Karasuyama, Sequential- and Parallel- Constrained Max-value Entropy Search via Information Lower Bound, Proceedings of The 39th International Conference on Machine Learning (ICML 2022), PMLR 162: 20960-20986, 2022 (Acceptance rate: 21.9%).
• Y. Inatsu, S. Takeno, M. Karasuyama and I. Takeuchi, Bayesian Optimization for Distributionally Robust Chance-constrained Problem, Proceedings of The 39th International Conference on Machine Learning (ICML 2022), PMLR 162:9602-9621, 2022 (Acceptance rate: 21.9%).
• S. Takeno, H. Fukuoka, Y. Tsukada, T. Koyama, M. Shiga, I. Takeuchi, and M. Karasuyama, Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization, Proceedings of the 37th International Conference on Machine Learning (ICML 2020), PMLR 119:9334-9345, 2020 (Acceptance rate 22%).
• S. Suzuki, S. Takeno, T. Tamura, K. Shitara, and M. Karasuyama, Multi-objective Bayesian Optimization using Pareto-frontier Entropy, Proceedings of the 37th International Conference on Machine Learning (ICML 2020), PMLR 119:9279-9288, 2020 (Acceptance rate 22%)
• D. V. N. Le, T. Sakuma, T. Ishiyama, H. Toda, K. Arai, M. Karasuyama, Y. Okubo, M. Sunaga, Y. Tabei, I. Takeuchi, Statistically Discriminative Sub-trajectory Mining with Multiple Testing Correction, the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2019 (Acceptance rate 21%).
• T. Yoshida, I. Takeuchi, and M. Karasuyama, Learning Interpretable Metric between Graphs: Convex Formulation and Computation with Graph Mining, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019), pp. 1026-1036, Anchorage, AK, USA, Aug. 04-08, 2019 (Acceptance rate 14%).
• T. Yoshida, I. Takeuchi, and M. Karasuyama, Safe Triplet Screening for Distance Metric Learning, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), pp. 2653-2662, London, United Kingdom, Aug. 19-23, 2018 (Acceptance rate 18%).
• M. Karasuyama, H. Mamitsuka, Factor Analysis on a Graph, Proceedings of the 21th International Conference on Artificial Intelligence and Statistics (AISTATS 2018), vol.84, pp.1117--1126, Canary Islands, Spain, Apr. 09-11, 2018 (Acceptance rate 33%).
• K. Nakagawa, S. Suzumura, M. Karasuyama, K. Tsuda, and I. Takeuchi, Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2016), pp.1785--1794, San Francisco, USA, Aug. 13-17, 2016 (Acceptance rate 18%).
• A. Shibagaki, M. Karasuyama, K. Hatano, I. Takeuchi, Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling, Proceedings of The 33rd International Conference on Machine Learning (ICML 2016), pp.1577-1586, New York, USA, Jun. 19-24, 2016 (Acceptance rate 24%).
• A. Shibagaki, Y. Suzuki, M. Karasuyama, I. Takeuchi, Regularization Path of Cross-Validation Error Lower Bounds Advances in Neural Information Processing Systems (NIPS) 28, pp.1666-1674, 2015 (Acceptance rate 22%).
• M. Karasuyama and H. Mamitsuka, Manifold-based Similarity Adaptation for Label Propagation, Advances in Neural Information Processing Systems (NIPS) 26, pp.1547-1555, 2013 (Acceptance rate 25%). [Code]
• M. Karasuyama, N. Harada, M. Sugiyama and I. Takeuchi, Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines, Machine Learning for Signal Processing (MLSP), 2011 IEEE International Workshop on, vol.xx, no.xx, pp.xx-xx, 18-21 Sept. 2011.
• M. Karasuyama, and I. Takeuchi, Suboptimal Solution Path Algorithm for Support Vector Machine, In L. Getoor and T. Scheffer eds., Proceedings of the 28th International Conference on Machine Learning (ICML), pp. 473-480, June, 2011 (Acceptance rate 26%).
• M. Karasuyama, and I. Takeuchi, Nonlinear Regularization Path for the Modified Huber loss Support Vector Machines, In International Joint Conference on Neural Networks (IJCNN), pp. 3099-3106, 2010.
• M. Karasuyama and I. Takeuchi, Multiple Incremental Decremental Learning of Support Vector Machines, In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta eds., Advances in Neural Information Processing Systems (NIPS) 22, pp. 907-915. 2009 (Acceptance rate 24%).
• M. Karasuyama, I. Takeuchi and R. Nakano, Reducing SVR Support Vectors by Using Backward Deletion, In Proceedings of the 12th international Conference on Knowledge-Based intelligent information and Engineering Systems (KES), Part III, Lecture Notes In Artificial Intelligence, vol. 5179. Springer-Verlag, Berlin, Heidelberg, pp. 76-83, 2008
• M. Karasuyama, and R. Nakano, Optimizing Sparse Kernel Ridge Regression Hyperparameters Based on Leave-one-out Cross-validation, In International Joint Conference on Neural Networks (IJCNN), pp. 3463-3468, 2008.
• M. Karasuyama, and R. Nakano, Optimizing SVR Hyperparameters via Fast Cross-Validation using AOSVR, In International Joint Conference on Neural Networks (IJCNN), pp. 1186-1191, 2007.
• M. Karasuyama, D. Kitakoshi and R. Nakano, Revised Optimizer of SVR Hyperparameters Minimizing Cross-Validation Error, In International Joint Conference on Neural Networks (IJCNN), pp. 711-718, 2006.

Online pre-print

Back↑

• S. Takeno, M. Nomura and M. Karasuyama, Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes, arXiv:2302.01513, 2023.
• S. Takeno, Y. Inatsu and M. Karasuyama, Randomized Gaussian Process Upper Confidence Bound with Tight Bayesian Regret Bounds, arXiv:2302.01511,2023.
• Y. Inatsu, S. Takeno, M. Karasuyama, I. Takeuchi, Bayesian Optimization for Distributionally Robust Chance-constrained Problem, arXiv:2201.13112, 2022
• S. Kusakawa, S. Takeno, Y. Inatsu, K. Kutsukake, S. Iwazaki, T. Nakano, T. Ujihara, M. Karasuyama, I. Takeuchi, Bayesian Optimization for Cascade-type Multi-stage Processes, arXiv:2111.08330, 2021
• S. Takeno, T. Tamura, K. Shitara, and M. Karasuyama, Sequential- and Parallel- Constrained Max-value Entropy Search via Information Lower Bound, arXiv:2102.09788, 2021
• K. Inoue, M. Karasuyama, R. Nakamura, M. Konno, D. Yamada, K. Mannen, T. Nagata, Y. Inatsu, K. Yura, O. Beja, H. Kandori, I. Takeuchi, Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design, bioRxiv 2020.04.21.052548, 2020
• S. Takeno, Y. Tsukada, H. Fukuoka, T. Koyama, M. Shiga, and M. Karasuyama, Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling, arXiv:2003.13428, 2020
• M. Karasuyama, H. Kasugai, T. Tamura, and K. Shitara, Computational Design of Stable and Highly Ion-conductive Materials using Multi-objective Bayesian Optimization: Case Studies on Diffusion of Oxygen and Lithium, arXiv:2003.09036, 2020
• T. Yoshida, I. Takeuchi and M. Karasuyama, Distance Metric Learning for Graph Structured Data, arXiv:2002.00727, 2020
• T. Tamura, and M. Karasuyama, Active-learning-based efficient prediction of ab-initio atomic energy: a case study on a Fe random grain boundary model with millions of atoms, arXiv:1912.04596, 2019
• Y. Inatsu, M. Karasuyama, K. Inoue, and I. Takeuchi, Active learning for level set estimation under cost-dependent input uncertainty, arXiv:1909.06064, 2019
• S. Suzuki, S. Takeno, T. Tamura, K. Shitara, and M. Karasuyama, Multi-objective Bayesian Optimization using Pareto-frontier Entropy, arXiv:1906.00127, 2019
• V. N. L. Duy, T. Sakuma, T. Ishiyama, H. Toda, K. Nishi, M. Karasuyama, Y. Okubo, M. Sunaga, Y. Tabei, I. Takeuchi, Statistically Discriminative Sub-trajectory Mining, arXiv:1905.01788, 2019
• S. Takeno, H. Fukuoka, Y. Tsukada, T. Koyama, M. Shiga, I. Takeuchi, and M. Karasuyama, Multi-fidelity Bayesian Optimization with Max-value Entropy Search, arXiv:1901.08275, 2019
• T. Yoshida, I. Takeuchi and M. Karasuyama, Safe Triplet Screening for Distance Metric Learning, arXiv:1802.03923, 2018
• M. Karasuyama, K. Inoue, H. Kandori, I. Takeuchi, Toward Machine Learning-based Data-driven Functional Protein Studies: Understanding Colour Tuning Rules and Predicting the Absorption Wavelengths of Microbial Rhodopsins, bioRxiv 226118, 2017
• K. Kanamori, K. Toyoura, J. Honda, K. Hattori, A. Seko, M. Karasuyama, K. Shitara, M. Shiga, A. Kuwabara, I. Takeuchi, Exploring a potential energy surface by machine learning for characterizing atomic transport, arXiv:1710.03468, 2017
• T. Yonezu, T. Tamura, I. Takeuchi, M. Karasuyama, Knowledge-Transfer based Cost-effective Search for Interface Structures: A Case Study on fcc-Al [110] Tilt Grain Boundary, arXiv:1708.03130, 2017
• K. Nakagawa, S. Suzumura, M. Karasuyama, K. Tsuda, I. Takeuchi, Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining, arXiv:1602.04548, 2016
• A. Shibagaki, M. Karasuyama, K. Hatano, Ichiro Takeuchi, Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling, arXiv:1602.02485, 2016
• K. Toyoura, D. Hirano, A. Seko, M. Shiga, A. Kuwabara, M. Karasuyama, K. Shitara, I. Takeuchi, A 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, arXiv:1512.00623, 2015
• S. Suzumura, K. Ogawa, M. Sugiyama, M. Karasuyama, Ichiro Takeuchi, Homotopy Continuation Approaches for Robust SV Classification and Regression arXiv:1507.03229, 2015
• K. Nakagawa, S. Suzumura, M. Karasuyama, K. Tsuda, I. Takeuchi, Safe Feature Pruning for Sparse High-Order Interaction Models, arXiv1506.08002, 2015
• A. Shibagaki, Y. Suzuki, M. Karasuyama, I. Takeuchi, Regularization Path of Cross-Validation Error Lower Bounds, arXiv:1502.02344, 2015
• M. Karasuyama, I. Takeuchi, Suboptimal Solution Path Algorithm for Support Vector Machine arXiv:1105.0471, 2011
• M. Karasuyama, N. Harada, M. Sugiyama, I. Takeuchi, Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines, arXiv:1009.4791, 2010

---

Biography

Back↑

Education

• Mar. 2006, Bachelor of Engineering, Nagoya Institute of Technology.
• Mar. 2008, Master of Engineering, Nagoya Institute of Technology.
• Mar 2011, Doctor of Engineering, Nagoya Institute of Technology.

Job Experience

• Apr. 2010 to Mar. 2011, JSPS Research Fellow (DC2)
• Apr. 2011 to Dec. 2011, JSPS Research Fellow (PD)/Postdoctoral Fellow, Tokyo Institute of Technology
• Jan. 2012 to Mar. 2015, Assistant Professor, Bioinformatics Center, Institute for Chemical Research, Kyoto University
• Apr. 2015 to Present, Assistant Professor, Department of Computer Science, Nagoya Institute of Technology