Mahito Sugiyama
Mahito Sugiyama
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Zitiert von
Zitiert von
Rapid distance-based outlier detection via sampling
M Sugiyama, K Borgwardt
Advances in neural information processing systems 26, 2013
Halting in random walk kernels
M Sugiyama, K Borgwardt
Advances in neural information processing systems 28, 2015
Fast and memory-efficient significant pattern mining via permutation testing
F Llinares-López, M Sugiyama, L Papaxanthos, K Borgwardt
Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015
Efficient network-guided multi-locus association mapping with graph cuts
CA Azencott, D Grimm, M Sugiyama, Y Kawahara, KM Borgwardt
Bioinformatics 29 (13), i171-i179, 2013
graphkernels: R and Python packages for graph comparison
M Sugiyama, ME Ghisu, F Llinares-López, K Borgwardt
Bioinformatics 34 (3), 530-532, 2018
Significant Subgraph Mining with Multiple Testing Correction
M Sugiyama, F Llinares-López, N Kasenburg, KM Borgwardt
2015 SIAM International Conference on Data Mining, 37-45, 2015
Genome-wide detection of intervals of genetic heterogeneity associated with complex traits
F Llinares-López, DG Grimm, DA Bodenham, U Gieraths, M Sugiyama, ...
Bioinformatics 31 (12), i240-i249, 2015
Artificial neural networks applied as molecular wave function solvers
PJ Yang, M Sugiyama, K Tsuda, T Yanai
Journal of Chemical Theory and Computation 16 (6), 3513-3529, 2020
Tensor balancing on statistical manifold
M Sugiyama, H Nakahara, K Tsuda
International Conference on Machine Learning, 3270-3279, 2017
Measuring Statistical Dependence via the Mutual Information Dimension
M Sugiyama, KM Borgwardt
The 23rd International Joint Conference on Artificial Intelligence (IJCAI …, 2013
Information decomposition on structured space
M Sugiyama, H Nakahara, K Tsuda
2016 IEEE International Symposium on Information Theory (ISIT), 575-579, 2016
A drive-by bridge inspection framework using non-parametric clusters over projected data manifolds
P Cheema, MM Alamdari, KC Chang, CW Kim, M Sugiyama
Mechanical Systems and Signal Processing 180, 109401, 2022
Multi-Task Feature Selection on Multiple Networks via Maximum Flows
M Sugiyama, CA Azencott, D Grimm, Y Kawahara, K Borgwardt
2014 SIAM International Conference on Data Mining, 199-207, 2014
Legendre decomposition for tensors
M Sugiyama, H Nakahara, K Tsuda
Advances in Neural Information Processing Systems 31, 2018
Finding Statistically Significant Interactions between Continuous Features.
M Sugiyama, KM Borgwardt
IJCAI, 3490-3498, 2019
A Fast and Flexible Clustering Algorithm Using Binary Discretization
M Sugiyama, A Yamamoto
2011 IEEE 11th International Conference on Data Mining (ICDM), 1212-1217, 2011
Bias-variance trade-off in hierarchical probabilistic models using higher-order feature interactions
S Luo, M Sugiyama
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4488-4495, 2019
Fast tucker rank reduction for non-negative tensors using mean-field approximation
K Ghalamkari, M Sugiyama
Advances in Neural Information Processing Systems 34, 443-454, 2021
A neural tangent kernel perspective of infinite tree ensembles
R Kanoh, M Sugiyama
arXiv preprint arXiv:2109.04983, 2021
Semi-supervised learning on closed set lattices
M Sugiyama, A Yamamoto
Intelligent Data Analysis 17 (3), 399-421, 2013
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