The University of Osaka
Machine Learning & Systems Laboratory

This is a yearly list of major publications since October 2022 when the laboratory at Osaka Univ. was established.
For the previous publications, please see each faculty member's page Member ».

2025〜

• †N. Ke, †R. Tanaka and Y. Kawahara (†equal contribution), "Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer Operators," in Proc. of the 28th Int'l Conf. on Artificial Intelligence and Statistics (AISTATS'25) (accepted) (the arXiv version is found [Here]).
• K.Ghalamkari, J.L. Hinrich, and M. Mørup, "Non-negative Tensor Low-rank Decompositions Through the Lens of Information Geometry," AAAI 25 Workshop on Connecting Low-rank Representations in AI, March. 2025.
• K. Fujii, K. Takeuchi, A. Kuribayashi, N. Takeishi, Y. Kawahara, and K. Takeda, "Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios," IEEE Transactions on Neural Networks and Learning Systems, Vol.36, No.2, pp.2103-2117, 2025.

2024

• M. Weissenbacher, E. Routis and Y. Kawahara, "Self-supervised Color Generalization in Reinforcement Learning," Transactions on Machine Learning Research (TMLR), 2024.
• Y. Inui, T. Konishi, and Y. Kawahara, "Learning with Almost Invariant Sets in Neural Oscillatory ODEs," in Proc. of the 31st Int'l Conf. on Neural Information Processing (ICONIP'24) (accepted).
• I. Sakata and Y. Kawahara, "Enhancing Spectral Analysis in Nonlinear Dynamics: An Approach Based on Pseudoeigenfunctions from Continuous Spectra," Scientific Reports, Vol.14, Article Number 19276, 2024.
• M. Ohnishi, I. Ishikawa, K. Lowrey, M. Ikeda, S.M. Kakade, and Y. Kawahara, "Koopman Spectrum Nonlinear Regulators and Efficient Online Learning," Transactions on Machine Learning Research (TMLR), 2024.
• R. Fukuma, K. Majima, Y. Kawahara, O. Yamashita, Y. Shiraishia, H. Kishima, and T. Yanagisawa, "Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition," Communications Biology, Vol.7, Article number 595, 2024.
• M. Weissenbacher, R. Agarwal, and Y. Kawahara, "SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning," in Proc. of the 41st Int'l Conf. on Machine Learning (ICML'24), PMLR 235:52695-52719, 2024.
• J. Bai and Y. Kawahara, ``MANet: Mixed Attention Network for Visual Explanation," New Generation Computing, DOI: 10.1007/s00354-024-00252-7, 2024.
• K. Fujii, K. Takeuchi, A. Kuribayashi, N. Takeishi, Y. Kawahara, and K. Takeda, "Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios," IEEE Transactions on Neural Networks and Learning Systems (accepted).
• H. Endo, S. Ikeda, K. Harada, H. Yamagata, T. Matsubara, K. Matsuo, Y. Kawahara, and O. Yamashita, "Manifold alteration between major depressive disorder and healthy control subjects using dynamic mode decomposition in resting-state fMRI data," Frontiers in Psychiatry, Vol.15, Article Number 1288808, 2024.
• K. Taga, Y. Kato, Y. Yamazaki, Y. Kawahara, and H. Nakao, "Dynamic mode decomposition for Koopman spectral analysis of elementary cellular automata," Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol.34, No.1, Article Number 013125, 2024 (the arXiv version is found [Here]).
• K. Fujii, K. Tsutsui, A. Scott, H. Nakahara, N. Takeishi, and Y. Kawahara, "Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations," in Proc. of the 16th Int'l Conf. on Agents and Artificial Intelligence (ICAART'24), Volume 2: ICAART, 27-39 (the arXiv version is found [Here]).
• K. Fujii, N. Takeishi, Y. Kawahara, and K. Takeda, "Decentralized Policy Learning with Partial Observation and Mechanical Constraints for Multi-person Modeling Learning Systems," Neural Networks, Vol.171, pp.40-52, 2024.

2023

• K. Ghalamkari, M. Sugiyama, and Y. Kawahara, "Many-body Approximation for Non-negative Tensors," Advances in Neural Information Processing Systems 36 (NeurIPS'23) (the arXiv version is found [Here]).
• N. Takeishi and Y. Kawahara, "A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores," Transactions on Machine Learning Research (TMLR), 2023.
• D. Bodenham and Y. Kawahara, "euMMD: Efficiently Computing the MMD Two-Sample Test Statistic for Univariate Data," Statistics and Computing, Vol.33, Article Number 110, 2023.
• T. Konishi and Y. Kawahara, "Stable Invariant Models with Koopman Spectra," Neural Networks, Vol.165, pp.393-405, 2023 (the arXiv version is found [Here]).
• T. Iwata and Y. Kawahara, "Neural dynamic mode decomposition for end-to-end modeling of nonlinear dynamics," Journal of Computational Dynamics, Vol.10, No.2, pp.268-280, 2023.

2022

(Since October 2022)

• N. Takeishi, K. Takeuchi, K. Fujii, and Y. Kawahara, "Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections," SIAM Journal on Applied Dynamical Systems, Vol.21, No.2, pp.1030–1058, 2022.
• H. Arai, M. Zribi, K. Oyoshi, K. Dassas, M. Huc, S. Sobue and T. Le Toan, "Quality Control of CyGNSS Reflectivity for Robust Spatiotemporal Detection of Tropical Wetlands," Remote Sensing, Vol.14, No.22, Article No.:5903, 2022.
• K. Fujii, K. Takeuchi, A. Kuribayashi, N. Takeishi, Y. Kawahara, and K. Takeda, "Estimating counterfactual treatment outcomes over time in multi-vehicle simulation," in Proc. Of the 30th ACM SIGPATIAL Int'l Conf. on Advances in Geographic Information Systems (SIGSPATIAL'22) (short paper), Article No.: 7, pp.1-4, 2022 (This paper received the Best Poster Award).