Publication

国内学会発表

Refereed full papers

Naoya Takeishi and Yoshinobu Kawahara.
Learning multiple nonlinear dynamical systems with side information.
In Proceedings of the 59th IEEE Conference on Decision and Control (CDC), 2020 (to appear).

Anand Srinivasan and Naoya Takeishi.
An MCMC method for uncertainty set generation via operator-theoretic metrics.
In Proceedings of the 59th IEEE Conference on Decision and Control (CDC), 2020 (to appear).

arXiv

Naoya Takeishi and Yoshinobu Kawahara.
Knowledge-based regularization in generative modeling.
In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI), pages 2390–2396, 2020.

* We will soon update the arXiv version with full experimental results.

paper (published ver.)

Yoshiyuki Anzai, Takehisa Yairi, Naoya Takeishi, Yuichi Tsuda, and Naoko Ogawa.
Visual localization for asteroid touchdown operation based on local image features.
Astrodynamics, 4:149–161, 2020.

Keisuke Fujii, Naoya Takeishi, Motokazu Hojo, Yuki Inaba, and Yoshinobu Kawahara.
Physically-interpretable classification of network dynamics for complex collective motions.
Scientific Reports, 10:3005, 2020.

paper (published ver.)

Naoya Takeishi.
Kernel learning for data-driven spectral analysis of Koopman operators.
In Proceedings of the 11th Asian Conference on Machine Learning (ACML), pages 956–971, 2019.

paper (published ver.)
poster

Keisuke Fujii, Naoya Takeishi, Benio Kibushi, Motoki Kouzaki, and Yoshinobu Kawahara.
Data-driven spectral analysis for coordinative structures in periodic human locomotion.
Scientific Reports, 9:16755, 2019.

paper (published ver.)

Riku Sasaki, Naoya Takeishi, Takehisa Yairi, and Koichi Hori.
Neural gray-box identification of nonlinear partial differential equations.
In the 16th Pacific Rim International Conference on Artificial Intelligence (PRICAI), Trends in Artificial Intelligence, Lecture Notes in Computer Science, 11671:309–321, 2019.

Ryo Sakagami, Naoya Takeishi, Takehisa Yairi, and Koichi Hori.
Visualization methods for spacecraft telemetry data using change-point detection and clustering.
Transactions of the Japan Society for Aeronautical and Space Sciences, Aerospace Technology Japan, 17(2):244–252, 2019.

Naoya Takeishi, Takehisa Yairi, and Yoshinobu Kawahara.
Factorially switching dynamic mode decomposition for Koopman analysis of time-variant systems.
In Proceedings of the 57th IEEE Conference on Decision and Control (CDC), pages 6402–6408, 2018.

paper (accepted ver.)
slides

Rem Hida, Naoya Takeishi, Takehisa Yairi, and Koichi Hori.
Dynamic and static topic model for analyzing time-series document collections.
In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), pages 516–520, 2018.

paper (published ver.)

Naoya Takeishi, Yoshinobu Kawahara, and Takehisa Yairi.
Learning Koopman invariant subspaces for dynamic mode decomposition.
In Advances in Neural Information Processing Systems 30, pages 1130–1140, 2017.

paper (published ver.)
poster
codes

Naoya Takeishi and Takehisa Yairi.
Visual monocular localization, mapping, and motion estimation of a rotating small celestial body.
Journal of Robotics and Mechatronics, 29(5):856–863, 2017.

paper (published ver.)

Naoya Takeishi, Yoshinobu Kawahara, and Takehisa Yairi.
Sparse nonnegative dynamic mode decomposition.
In Proceedings of the 24th IEEE International Conference on Image Processing (ICIP), pages 2682–2686, 2017.

paper (accepted ver.)

Naoya Takeishi, Yoshinobu Kawahara, and Takehisa Yairi.
Subspace dynamic mode decomposition for stochastic Koopman analysis.
Physical Review E, 96(3):033310, 2017.

* The published version has a typo in the main algorithm, which is fixed in the arXiv version.

arXiv
codes

Naoya Takeishi, Yoshinobu Kawahara, Yasuo Tabei, and Takehisa Yairi.
Bayesian dynamic mode decomposition.
In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), pages 2814–2821, 2017.

paper (published ver.)
slides
codes

Takehisa Yairi, Naoya Takeishi, Tetsuo Oda, Yuta Nakajima, Naoki Nishimura, and Noboru Takata.
A data-driven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction.
IEEE Transactions on Aerospace and Electronic Systems, 53(3):1384–1401, 2017.

paper (published ver.)

Naoya Takeishi, Takehisa Yairi, Naoki Nishimura, Yuta Nakajima, and Noboru Takata.
Dynamic grouped mixture models for intermittent multivariate sensor data.
In the 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Advances in Knowledge Discovery and Data Mining, Lecture Notes in Artificial Intelligence, 9652:221–232, 2016.

paper (accepted ver.)
slides

Naoya Takeishi, Takehisa Yairi, Yuichi Tsuda, Fuyuto Terui, Naoko Ogawa, and Yuya Mimasu.
Simultaneous estimation of shape and motion of an asteroid for automatic navigation.
In Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), pages 2861–2866, 2015.

paper (accepted ver.)
poster

Naoya Takeishi, Akira Tanimoto, Takehisa Yairi, Yuichi Tsuda, Fuyuto Terui, Naoko Ogawa, and Yuya Mimasu.
Evaluation of interest-region detectors and descriptors for automatic landmark tracking on asteroids.
Transactions of the Japan Society for Aeronautical and Space Sciences, 58(1):45–53, 2015.

paper (published ver.)

Naoya Takeishi and Takehisa Yairi.
Anomaly detection from multivariate time-series with sparse representation.
In Proceedings of 2014 IEEE International Conference on Systems, Man and Cybernetics (SMC), pages 2651–2656, 2014.

paper (accepted ver.)

Akira Tanimoto, Naoya Takeishi, Takehisa Yairi, Yuichi Tsuda, Fuyuto Terui, Naoko Ogawa, and Yuya Mimasu.
Fast estimation of asteroid shape and motion for spacecraft navigation.
In Proceedings of 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), pages 1550–1555, 2013.

Preprints

Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara, and Kazuya Takeda.
Policy learning with partial observation and mechanical constraints for multi-person modeling.
arXiv:2007.03155, 2020.

arXiv

Naoya Takeishi and Yoshinobu Kawahara.
Learning dynamics models with stable invariant sets.
arXiv:2006.08935, 2020.

* Current version includes some errors in Proposition 1 and Eq. (8). We will soon correct them.

arXiv

Naoya Takeishi and Yoshinobu Kawahara.
On anomaly interpretation via Shapley values.
arXiv:2004.04464, 2020.

arXiv

Chun Fui Liew, Danielle DeLatte, Naoya Takeishi, and Takehisa Yairi.
Recent developments in aerial robotics: A survey and prototypes overview.
arXiv:1711.10085, 2017.

arXiv

Presentations

Naoya Takeishi.
Towards intelligent asteroid exploration: Visual SLAM in space.
Invited talk at the 23rd SANKEN International Symposium, Awaji, Japan, 2020.

Keisuke Fujii, Naoya Takeishi, and Yoshinobu Kawahara.
Interpretable classification of complex collective motions using graph dynamic mode decomposition.
Workshop on Machine Learning for Trajectory, Activity, and Behavior, Nagoya, Japan, 2019.

Naoya Takeishi.
Shapley values of reconstruction errors of PCA for explaining anomaly detection.
In Proceedings of ICDM Workshops, pages 793–798, 2019.
Workshop on Learning and Mining with Industrial Data, Beijing, P.R.China, November 2019.

* The published version has typos in Eqs. (14) and (18), which are fixed in the arXiv version.

arXiv
slides
codes

Naoya Takeishi and Kosuke Akimoto.
Knowledge-based distant regularization in learning probabilistic models.
The 8th International Workshop on Statistical Relational AI, Stockholm, Sweden, July 2018.

arXiv

Taichi Kitamura, Naoya Takeishi, Takehisa Yairi, and Koichi Hori.
Abnormal sound detection for rotary parts in noisy environment by one-class SVM and non-negative matrix factorization.
Asia Pacific Conference of the Prognostics and Health Management Society, Jeju, Korea, July 2017.

paper (published ver.)

Riku Sasaki, Naoya Takeishi, Takehisa Yairi, Koichi Hori, Kazunari Ide, and Hiroyoshi Kubo.
A health monitoring method for wind power generators with hidden Markov and probabilistic principal components analysis models.
Asia Pacific Conference of the Prognostics and Health Management Society, Jeju, Korea, July 2017.

paper (published ver.)

Naoya Takeishi and Takehisa Yairi.
Dynamic visual simultaneous localization and mapping for asteroid exploration.
The 13th International Symposium on Artificial Intelligence, Robotics and Automation in Space, Beijing, P.R.China, June 2016.

paper (accepted ver.)
slides

Kosuke Akimoto, Naoya Takeishi, Takehisa Yairi, Koichi Hori, Naoki Nishimura, and Noboru Takata.
Tree-based nonparametric prediction of normal sensor measurement range using temporal information.
The 13th International Symposium on Artificial Intelligence, Robotics and Automation in Space, Beijing, P.R.China, June 2016.

Naoya Takeishi.
Automatic landmark recognition for asteroid by image features.
The 29th International Symposium on Space Technology and Science, Nagoya, Japan, June 2013.

paper (published ver.)