Our team is affiliated with the RIKEN Center for Advanced Intelligence Project (AIP), located in Tokyo. You can also visit our official pages on the RIKEN website and AIP website.
Research
Modern machine learning technologies often demand vast amounts of data, large-scale models, and considerable computational resources, which also results in challenges related to the reliability and interpretability of well-trained models. Our team aims to develop efficient, robust, and interpretable machine learning models and algorithms, along with their theoretical analysis. We focus on several key directions including tensor methods for machine learning, robust and interpretable machine learning and quantum machine learning. Our research spans various areas such as self-supervised learning, unsupervised representation learning, multi-modal learning and deep generative models. We also welcome collaborations in practical domains such as brain-computer interfaces, medical diagnosis, and etc.
Research Topics
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Tensor Methods for Machine Learning
Our research lies in the intersection of tensor networks and machine learning. We investigate tensor network theory with its algorithms and leverage tensor network representations to address critical challenges in deep learning, such as data and parameter efficiency, adversarial robustness and interpretability of deep generative models, as well as the theoretical understanding of tensor-based learning methods.
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Trustworthy Machine Learning
Our research in trustworthy machine learning focuses on the robustness and interpretability. We aim to develop robust models that can handle noisy and incomplete data, as well as adversarial attacks, while mitigating potential performance degradation. In terms of interpretability, we intend to design inherently interpretable models and explore the relationship between robustness and interpretability.
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Quantum Machine Learning
Our research focuses on quantum-inspired machine learning models and hybrid quantum-classical algorithms, aiming to harness quantum advantages to enhance machine learning technologies. We intend to investigate tensor networks for data encoding, quantum circuit compression, and quantum algorithms. This research is supported by the RIKEN Quantum project.
For more information, please visit our Publications and Events pages. We are actively seeking collaborations and welcome you to join us. If you're interested in joining our team, please fill out this Google Form.
News
[Link] Qibin Zhao is organizing a Workshop on Current and Future Computational Approaches to Quantum Many-Body Systems on 2 - 5 March, Okinawa, Japan.
Jan 2026[Link] Mingyuan Bai is organizing a ICLR'26 Workshop on Deep Generative Model in Machine Learning on 26 April., Rio de Janeiro, Brazil.
Jan 2026Our team has two papers accepted by ICLR 2026.
Jan 2026[Link] We are pleased to welcome Prof. Bao-Liang Lu (SJTU) to our team for an academic visit.
Nov 2025[Link] We are pleased to welcome Prof. Yongsheng Gao (Griffith University) to our team for an academic visit.
Sep 2025Our team has two papers accepted by NeurIPS 2025.
For more past news, please visit News List