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, 467-475, 2013
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
Halting in random walk kernels
M Sugiyama, K Borgwardt
Advances in neural information processing systems, 1639-1647, 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
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
graphkernels: R and Python packages for graph comparison
M Sugiyama, ME Ghisu, F Llinares-López, K Borgwardt
Bioinformatics 34 (3), 530-532, 2018
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
Tensor balancing on statistical manifold
M Sugiyama, H Nakahara, K Tsuda
arXiv preprint arXiv:1702.08142, 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 Fast and Flexible Clustering Algorithm Using Binary Discretization
M Sugiyama, A Yamamoto
2011 IEEE 11th International Conference on Data Mining (ICDM), 1212-1217, 2011
Mining significant subgraphs with multiple testing correction
M Sugiyama, FL López, N Kasenburg, KM Borgwardt
SIAM Data Mining (SDM), 2015
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, 4488-4495, 2019
Significant Pattern Mining on Continuous Variables
M Sugiyama, KM Borgwardt
arXiv preprint arXiv:1702.08694, 2017
Learning figures with the Hausdorff metric by fractals—towards computable binary classification
M Sugiyama, E Hirowatari, H Tsuiki, A Yamamoto
Machine learning 90 (1), 91-126, 2013
Learning figures with the hausdorff metric by fractals
M Sugiyama, E Hirowatari, H Tsuiki, A Yamamoto
International Conference on Algorithmic Learning Theory, 315-329, 2010
Finding Statistically Significant Interactions between Continuous Features.
M Sugiyama, KM Borgwardt
IJCAI, 3490-3498, 2019
Legendre decomposition for tensors
M Sugiyama, H Nakahara, K Tsuda
Advances in Neural Information Processing Systems, 8811-8821, 2018
Semi-supervised learning for mixed-type data via formal concept analysis
M Sugiyama, A Yamamoto
International Conference on Conceptual Structures, 284-297, 2011
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