Publications - KAWAII AI lab

Preprints

See also Google Scholar.

• Atsushi Suzuki, Yulan He, Feng Tian, and Zhongyuan Wang.
  "Hallucinations are inevitable but statistically negligible."
  arXiv:2502.12187, 2025. Brief introduction on X
• Atsushi Suzuki, Kota Fukuzawa, and Kenji Yamanishi.
  "Foundation of Calculating Normalized Maximum Likelihood for Continuous Probability Models."
  arXiv:2409.08387, 2024. Brief introduction on X

Refereed Publications

See also Google Scholar.

• Ryo Yuki, Atsushi Suzuki, and Kenji Yamanishi.
  "Dimensionality and Curvature Selection of Graph Embedding using DNML Code-Length."
  In Proceedings of the 23rd IEEE International Conference on Data Mining (ICDM), 2023, Accepted.
• Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, and Kenji Yamanishi.
  "Tight and fast generalization error bound of graph embedding in metric space."
  In Proceedings of the 40th International Conference on Machine Learning (ICML), 2023, Accepted.
• Zipei Yan, Linchuan Xu, Atsushi Suzuki, Jing Wang, Jiannong Cao, and Jun Huang.
  "RGB Color Model Aware Computational Color Naming and Its Application to Data Augmentation."
  In Proceedings of 2022 IEEE International Conference on Big Data (BigData), pages 1172-1181, 2022.
• Atsushi Suzuki and Kenji Yamanishi.
  "Fourier-analysis-based form of normalized maximum likelihood: Exact formula and relation to complex Bayesian prior."
  IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 67(9):6164–6178, 2021.
• Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, and Marc Cavazza.
  "Generalization error bounds for graph embedding using negative sampling: Linear vs hyperbolic."
  In Proceedings of the 35th Annual Conference on Neural Information Processing Systems (NeurIPS), pages 1243–1255, 2021.
• Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, and Marc Cavazza.
  "Generalization error bound for hyperbolic ordinal embedding."
  In Proceedings of the 38th International Conference on Machine Learning (ICML), pages 10011–10021. PMLR, 2021.
• Atsushi Suzuki, Jing Wang, Feng Tian, Atsushi Nitanda, and Kenji Yamanishi.
  "Hyperbolic ordinal embedding."
  In Proceedings of the 11th Asian Conference on Machine Learning (ACML), 2019.
• Jing Wang, Atsushi Suzuki, Linchuan Xu, Feng Tian, Liang Yang, and Kenji Yamanishi.
  "Orderly subspace clustering."
  In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI), 2019.
• Jing Wang, Linchuan Xu, Feng Tian, Atsushi Suzuki, Changqing Zhang, and Kenji Yamanishi.
  "Attributed subspace clustering."
  In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019.
• Atsushi Suzuki and Kenji Yamanishi.
  "Exact calculation of normalized maximum likelihood code length using Fourier analysis."
  In Proceedings of the 2018 IEEE International Symposium on Information Theory (ISIT), pages 1211–1215. IEEE, 2018.
• Atsushi Suzuki, Kohei Miyaguchi, and Kenji Yamanishi.
  "Structure selection for convolutive non-negative matrix factorization using normalized maximum likelihood coding."
  In Proceedings of the 16th IEEE International Conference on Data Mining (ICDM), Barcelona, Dec. 2016.