Fast estimation of asteroid shape and motion for spacecraft navigation. - Akira Tanimoto; Naoya Takeishi; Takehisa Yairi; Yuichi Tsuda; Fuyuto Terui; Naoko Ogawa; Yuya Mimasu (ROBIO, 2013)

Anomaly detection from multivariate time-series with sparse representation. - Naoya Takeishi; Takehisa Yairi (SMC, 2014)

Simultaneous estimation of shape and motion of an asteroid for automatic navigation. - Naoya Takeishi; Takehisa Yairi; Yuichi Tsuda; Fuyuto Terui; Naoko Ogawa; Yuya Mimasu (ICRA, 2015)

Dynamic Grouped Mixture Models for Intermittent Multivariate Sensor Data. - Naoya Takeishi; Takehisa Yairi; Naoki Nishimura; Yuta Nakajima; Noboru Takata (PAKDD (2), 2016)

Sparse nonnegative dynamic mode decomposition. - Naoya Takeishi; Yoshinobu Kawahara; Takehisa Yairi (ICIP, 2017)

Bayesian Dynamic Mode Decomposition. - Naoya Takeishi; Yoshinobu Kawahara; Yasuo Tabei; Takehisa Yairi (IJCAI, 2017)

Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition. - Naoya Takeishi; Yoshinobu Kawahara; Takehisa Yairi (NIPS, 2017)

Recent Developments in Aerial Robotics: A Survey and Prototypes Overview. - Chun Fui Liew; Danielle DeLatte; Naoya Takeishi; Takehisa Yairi (CoRR, 2017)

Visual Monocular Localization, Mapping, and Motion Estimation of a Rotating Small Celestial Body. - Naoya Takeishi; Takehisa Yairi (J. Robotics Mechatronics, 2017)

A Data-Driven Health Monitoring Method for Satellite Housekeeping Data Based on Probabilistic Clustering and Dimensionality Reduction. - Takehisa Yairi; Naoya Takeishi; Tetsuo Oda; Yuta Nakajima; Naoki Nishimura; Noboru Takata (IEEE Trans. Aerosp. Electron. Syst., 2017)

Dynamic and Static Topic Model for Analyzing Time-Series Document Collections. - Rem Hida; Naoya Takeishi; Takehisa Yairi; Koichi Hori (ACL (2), 2018)

Factorially Switching Dynamic Mode Decomposition for Koopman Analysis of Time-Variant Systems. - Naoya Takeishi; Takehisa Yairi; Yoshinobu Kawahara (CDC, 2018)

Knowledge-Based Distant Regularization in Learning Probabilistic Models. - Naoya Takeishi; Kosuke Akimoto (CoRR, 2018)

Kernel Learning for Data-Driven Spectral Analysis of Koopman Operators. - Naoya Takeishi (ACML, 2019)

Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection. - Naoya Takeishi (ICDM Workshops, 2019)

Neural Gray-Box Identification of Nonlinear Partial Differential Equations. - Riku Sasaki; Naoya Takeishi; Takehisa Yairi; Koichi Hori (PRICAI (2), 2019)

Regularizing Generative Models Using Knowledge of Feature Dependence. - Naoya Takeishi; Yoshinobu Kawahara (CoRR, 2019)

Physically-interpretable classification of network dynamics for complex collective motions. - Keisuke Fujii 001; Naoya Takeishi; Motokazu Hojo; Yuki Inaba; Yoshinobu Kawahara (CoRR, 2019)

An MCMC Method for Uncertainty Set Generation via Operator-Theoretic Metrics. - Anand Srinivasan; Naoya Takeishi (CDC, 2020)

Learning Multiple Nonlinear Dynamical Systems with Side Information. - Naoya Takeishi; Yoshinobu Kawahara (CDC, 2020)

Knowledge-Based Regularization in Generative Modeling. - Naoya Takeishi; Yoshinobu Kawahara (IJCAI, 2020)

On Anomaly Interpretation via Shapley Values. - Naoya Takeishi; Yoshinobu Kawahara (CoRR, 2020)

Policy learning with partial observation and mechanical constraints for multi-person modeling. - Keisuke Fujii 001; Naoya Takeishi; Yoshinobu Kawahara; Kazuya Takeda (CoRR, 2020)

Learning Dynamics Models with Stable Invariant Sets. - Naoya Takeishi; Yoshinobu Kawahara (AAAI, 2021)

Learning interaction rules from multi-animal trajectories via augmented behavioral models. - Keisuke Fujii 001; Naoya Takeishi; Kazushi Tsutsui; Emyo Fujioka; Nozomi Nishiumi; Ryoya Tanaka; Mika Fukushiro; Kaoru Ide; Hiroyoshi Kohno; Ken Yoda; Susumu Takahashi; Shizuko Hiryu; et al. (NeurIPS, 2021)

Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling. - Naoya Takeishi; Alexandros Kalousis (NeurIPS, 2021)

Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections. - Naoya Takeishi; Keisuke Fujii 001; Koh Takeuchi 001; Yoshinobu Kawahara (CoRR, 2021)

Asteroid Flyby Cycler Trajectory Design Using Deep Neural Networks. - Naoya Ozaki; Kanta Yanagida; Takuya Chikazawa; Nishanth Pushparaj; Naoya Takeishi; Ryuki Hyodo (CoRR, 2021)

Estimating counterfactual treatment outcomes over time in multi-vehicle simulation. - Keisuke Fujii 001; Koh Takeuchi 001; Atsushi Kuribayashi; Naoya Takeishi; Yoshinobu Kawahara; Kazuya Takeda (SIGSPATIAL/GIS, 2022)

Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios. - Keisuke Fujii 001; Koh Takeuchi 001; Atsushi Kuribayashi; Naoya Takeishi; Yoshinobu Kawahara; Kazuya Takeda (CoRR, 2022)

Discriminant Dynamic Mode Decomposition for Labeled Spatiotemporal Data Collections. - Naoya Takeishi; Keisuke Fujii 001; Koh Takeuchi 001; Yoshinobu Kawahara (SIAM J. Appl. Dyn. Syst., 2022)

Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models. - Naoya Takeishi; Alexandros Kalousis (AISTATS, 2023)

Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability. - Maciej Falkiewicz; Naoya Takeishi; Imahn Shekhzadeh; Antoine Wehenkel; Arnaud Delaunoy; Gilles Louppe; Alexandros Kalousis (NeurIPS, 2023)

Sample-Efficient On-Policy Imitation Learning from Observations. - João A. Cândido Ramos; Lionel Blondé; Naoya Takeishi; Alexandros Kalousis (CoRR, 2023)

A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores. - Naoya Takeishi; Yoshinobu Kawahara (Trans. Mach. Learn. Res., 2023)

Adaptive Action Supervision in Reinforcement Learning from Real-World Multi-Agent Demonstrations. - Keisuke Fujii 001; Kazushi Tsutsui; Atom Scott; Hiroshi Nakahara; Naoya Takeishi; Yoshinobu Kawahara (ICAART (2), 2024)

Mimicking Better by Matching the Approximate Action Distribution. - João A. Cândido Ramos; Lionel Blondé; Naoya Takeishi; Alexandros Kalousis (ICML, 2024)

Kolmogorov-Smirnov GAN. - Maciej Falkiewicz; Naoya Takeishi; Alexandros Kalousis (CoRR, 2024)

Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling. - Keisuke Fujii 001; Naoya Takeishi; Yoshinobu Kawahara; Kazuya Takeda (Neural Networks, 2024)

Estimating Counterfactual Treatment Outcomes Over Time in Complex Multiagent Scenarios. - Keisuke Fujii 001; Koh Takeuchi 001; Atsushi Kuribayashi; Naoya Takeishi; Yoshinobu Kawahara; Kazuya Takeda (IEEE Trans. Neural Networks Learn. Syst., 2025)

A Temporal Difference Method for Stochastic Continuous Dynamics. - Haruki Settai; Naoya Takeishi; Takehisa Yairi (CoRR, 2025)

Simulation-Efficient Cosmological Inference with Multi-Fidelity SBI. - Leander Thiele; Adrian E. Bayer; Naoya Takeishi (CoRR, 2025)