| Rapid distance-based outlier detection via sampling M Sugiyama, K Borgwardt Advances in neural information processing systems 26, 2013 | 183 | 2013 |
| Halting in random walk kernels M Sugiyama, K Borgwardt Advances in neural information processing systems 28, 2015 | 150 | 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 | 82 | 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 | 81 | 2013 |
| graphkernels: R and Python packages for graph comparison M Sugiyama, ME Ghisu, F Llinares-López, K Borgwardt Bioinformatics 34 (3), 530-532, 2018 | 70 | 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 | 53 | 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 | 48 | 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 | 34 | 2020 |
| Tensor balancing on statistical manifold M Sugiyama, H Nakahara, K Tsuda International Conference on Machine Learning, 3270-3279, 2017 | 32 | 2017 |
| 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 | 26 | 2022 |
| Measuring Statistical Dependence via the Mutual Information Dimension M Sugiyama, KM Borgwardt The 23rd International Joint Conference on Artificial Intelligence (IJCAI …, 2013 | 24 | 2013 |
| Information decomposition on structured space M Sugiyama, H Nakahara, K Tsuda 2016 IEEE International Symposium on Information Theory (ISIT), 575-579, 2016 | 23 | 2016 |
| Legendre decomposition for tensors M Sugiyama, H Nakahara, K Tsuda Advances in Neural Information Processing Systems 31, 2018 | 20 | 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 | 19 | 2014 |
| Finding Statistically Significant Interactions between Continuous Features. M Sugiyama, KM Borgwardt IJCAI, 3490-3498, 2019 | 11 | 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 | 11 | 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 | 10 | 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 | 9 | 2021 |
| A neural tangent kernel perspective of infinite tree ensembles R Kanoh, M Sugiyama arXiv preprint arXiv:2109.04983, 2021 | 7 | 2021 |
| Fast rank-1 NMF for missing data with KL divergence K Ghalamkari, M Sugiyama International Conference on Artificial Intelligence and Statistics, 2927-2940, 2022 | 6 | 2022 |
