| Scalable reinforcement learning for plant-wide control of vinyl acetate monomer process L Zhu, Y Cui, G Takami, H Kanokogi, T Matsubara Control Engineering Practice 97, 104331, 2020 | 47 | 2020 |
| Factorial kernel dynamic policy programming for vinyl acetate monomer plant model control Y Cui, L Zhu, M Fujisaki, H Kanokogi, T Matsubara 2018 IEEE 14th International Conference on Automation Science and …, 2018 | 29 | 2018 |
| Multi-tier platform for cognizing massive electroencephalogram Z Chen, L Zhu, Z Yang, R Zhang arXiv preprint arXiv:2204.09840, 2022 | 11 | 2022 |
| Alleviating parameter-tuning burden in reinforcement learning for large-scale process control L Zhu, G Takami, M Kawahara, H Kanokogi, T Matsubara Computers & Chemical Engineering 158, 107658, 2022 | 11 | 2022 |
| Cyclic policy distillation: Sample-efficient sim-to-real reinforcement learning with domain randomization Y Kadokawa, L Zhu, Y Tsurumine, T Matsubara Robotics and Autonomous Systems 165, 104425, 2023 | 8 | 2023 |
| Ensuring monotonic policy improvement in entropy-regularized value-based reinforcement learning L Zhu, T Matsubara arXiv preprint arXiv:2008.10806, 2020 | 7 | 2020 |
| Adaptive spike-like representation of eeg signals for sleep stages scoring L Zhu, Z Yang, K Odani, G Shi, Y Kan, Z Chen, R Zhang 2022 44th Annual International Conference of the IEEE Engineering in …, 2022 | 6 | 2022 |
| Automated cancer subtyping via vector quantization mutual information maximization Z Chen, L Zhu, Z Yang, T Matsubara Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 5 | 2022 |
| Dynamic actor-advisor programming for scalable safe reinforcement learning L Zhu, Y Cui, T Matsubara 2020 IEEE International Conference on Robotics and Automation (ICRA), 10681 …, 2020 | 5 | 2020 |
| Learning vector quantized representation for cancer subtypes identification Z Chen, Z Yang, L Zhu, P Gao, T Matsubara, S Kanaya, M Altaf-Ul-Amin Computer Methods and Programs in Biomedicine 236, 107543, 2023 | 4 | 2023 |
| Cancer subtyping by improved transcriptomic features using vector quantized variational autoencoder Z Chen, Z Yang, L Zhu, G Shi, K Yue, T Matsubara, S Kanaya, ... arXiv preprint arXiv:2207.09783, 2022 | 4 | 2022 |
| Automated sleep staging via parallel frequency-cut attention Z Chen, Z Yang, L Zhu, W Chen, T Tamura, N Ono, MD Altaf-Ul-Amin, ... IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023 | 3 | 2023 |
| Hierarchical categorical generative modeling for multi-omics cancer subtyping Z Yang, L Zhu, C Li, Z Chen, N Ono, M Altaf-Ul-Amin, S Kanaya 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2022 | 3 | 2022 |
| Cancer subtyping via embedded unsupervised learning on transcriptomics data Z Yang, L Zhu, Z Chen, M Huang, N Ono, MD Altaf-Ul-Amin, S Kanaya 2022 44th Annual International Conference of the IEEE Engineering in …, 2022 | 3 | 2022 |
| A two-view EEG representation for brain cognition by composite temporal-spatial contrastive learning Z Chen, L Zhu, H Jia, T Matsubara Proceedings of the 2023 SIAM International Conference on Data Mining (SDM …, 2023 | 1 | 2023 |
| Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning L Zhu, Z Chen, E Uchibe, T Matsubara arXiv preprint arXiv:2205.07885, 2022 | 1 | 2022 |
| Geometric Value Iteration: Dynamic Error-Aware KL Regularization for Reinforcement Learning T Kitamura, L Zhu, T Matsubara Asian Conference on Machine Learning, 918-931, 2021 | 1 | 2021 |
| Cautious actor-critic L Zhu, T Kitamura, M Takamitsu Asian Conference on Machine Learning, 220-235, 2021 | 1 | 2021 |
| General Munchausen Reinforcement Learning with Tsallis Kullback-Leibler Divergence L Zhu, Z Chen, M Schlegel, M White Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Cautious policy programming: exploiting KL regularization for monotonic policy improvement in reinforcement learning L Zhu, T Matsubara Machine Learning 112 (11), 4527-4562, 2023 | | 2023 |
Takamitsu MatsubaraProfessor, Nara Institute of Science and TechnologyVerified email at is.naist.jp
