| Generalization error bound for hyperbolic ordinal embedding A Suzuki, A Nitanda, J Wang, L Xu, K Yamanishi, M Cavazza International Conference on Machine Learning, 10011-10021, 2021 | 13 | 2021 |
| Generalization bounds for graph embedding using negative sampling: Linear vs hyperbolic A Suzuki, A Nitanda, L Xu, K Yamanishi, M Cavazza Advances in Neural Information Processing Systems 34, 1243-1255, 2021 | 12 | 2021 |
| Exact calculation of normalized maximum likelihood code length using Fourier analysis A Suzuki, K Yamanishi 2018 IEEE International Symposium on Information Theory (ISIT), 1211-1215, 2018 | 11 | 2018 |
| Hyperbolic ordinal embedding A Suzuki, J Wang, F Tian, A Nitanda, K Yamanishi Asian Conference on Machine Learning, 1065-1080, 2019 | 6 | 2019 |
| Orderly subspace clustering J Wang, A Suzuki, L Xu, F Tian, L Yang, K Yamanishi Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5264-5272, 2019 | 6 | 2019 |
| Structure selection for convolutive non-negative matrix factorization using normalized maximum likelihood coding A Suzuki, K Miyaguchi, K Yamanishi 2016 IEEE 16th International Conference on Data Mining (ICDM), 1221-1226, 2016 | 5 | 2016 |
| Fourier-analysis-based form of normalized maximum likelihood: Exact formula and relation to complex bayesian prior A Suzuki, K Yamanishi IEEE Transactions on Information Theory 67 (9), 6164-6178, 2021 | 3 | 2021 |
| Attributed subspace clustering J Wang, L Xu, F Tian, A Suzuki, C Zhang, K Yamanishi The 28th International Joint Conference on Artificial Intelligence, 3719-3725, 2019 | 3 | 2019 |
| RGB color model aware computational color naming and its application to data augmentation Z Yan, L Xu, A Suzuki, J Wang, J Cao, J Huang 2022 IEEE International Conference on Big Data (Big Data), 1172-1181, 2022 | 2 | 2022 |
| Tight and fast generalization error bound of graph embedding in metric space A Suzuki, A Nitanda, T Suzuki, J Wang, F Tian, K Yamanishi The 40th International Conference on Machine Learning 202, 33268--33284, 2023 | | 2023 |
