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ORCIDNaoya Takeishi
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2020 – today
- 2024
[j5]Keisuke Fujii
, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda:
Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling. Neural Networks 171: 40-52 (2024)- [c23]Keisuke Fujii, Kazushi Tsutsui, Atom Scott, Hiroshi Nakahara, Naoya Takeishi, Yoshinobu Kawahara:
Adaptive Action Supervision in Reinforcement Learning from Real-World Multi-Agent Demonstrations. ICAART (2) 2024: 27-39 - [c22]João A. Cândido Ramos, Lionel Blondé, Naoya Takeishi, Alexandros Kalousis:
Mimicking Better by Matching the Approximate Action Distribution. ICML 2024 - [i20]Maciej Falkiewicz, Naoya Takeishi, Alexandros Kalousis:
Kolmogorov-Smirnov GAN. CoRR abs/2406.19948 (2024) - 2023
- [j4]Naoya Takeishi, Yoshinobu Kawahara:
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores. Trans. Mach. Learn. Res. 2023 (2023) - [c21]Naoya Takeishi, Alexandros Kalousis:
Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models. AISTATS 2023: 4089-4100 - [c20]Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh, Antoine Wehenkel, Arnaud Delaunoy, Gilles Louppe, Alexandros Kalousis:
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability. NeurIPS 2023 - [i19]Keisuke Fujii, Kazushi Tsutsui, Atom Scott, Hiroshi Nakahara, Naoya Takeishi, Yoshinobu Kawahara:
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations. CoRR abs/2305.13030 (2023) - [i18]João A. Cândido Ramos, Lionel Blondé, Naoya Takeishi, Alexandros Kalousis:
Sample-Efficient On-Policy Imitation Learning from Observations. CoRR abs/2306.09805 (2023) - [i17]Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh, Antoine Wehenkel, Arnaud Delaunoy, Gilles Louppe, Alexandros Kalousis:
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability. CoRR abs/2310.13402 (2023) - 2022
- [j3]Naoya Takeishi, Keisuke Fujii, Koh Takeuchi, Yoshinobu Kawahara:
Discriminant Dynamic Mode Decomposition for Labeled Spatiotemporal Data Collections. SIAM J. Appl. Dyn. Syst. 21(2): 1030-1058 (2022) - [c19]Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda:
Estimating counterfactual treatment outcomes over time in multi-vehicle simulation. SIGSPATIAL/GIS 2022: 7:1-7:4 - [i16]Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda:
Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios. CoRR abs/2206.01900 (2022) - [i15]Naoya Takeishi, Alexandros Kalousis:
Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models. CoRR abs/2210.13103 (2022) - 2021
- [c18]Naoya Takeishi, Yoshinobu Kawahara:
Learning Dynamics Models with Stable Invariant Sets. AAAI 2021: 9782-9790 - [c17]Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu, Yoshinobu Kawahara:
Learning interaction rules from multi-animal trajectories via augmented behavioral models. NeurIPS 2021: 11108-11122 - [c16]Naoya Takeishi, Alexandros Kalousis:
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling. NeurIPS 2021: 14809-14821 - [i14]Naoya Takeishi, Keisuke Fujii, Koh Takeuchi, Yoshinobu Kawahara:
Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections. CoRR abs/2102.09973 (2021) - [i13]Naoya Takeishi, Alexandros Kalousis:
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling. CoRR abs/2102.13156 (2021) - [i12]Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu, Yoshinobu Kawahara:
Learning interaction rules from multi-animal trajectories via augmented behavioral models. CoRR abs/2107.05326 (2021) - [i11]Naoya Ozaki, Kanta Yanagida, Takuya Chikazawa, Nishanth Pushparaj, Naoya Takeishi, Ryuki Hyodo:
Asteroid Flyby Cycler Trajectory Design Using Deep Neural Networks. CoRR abs/2111.11858 (2021) - 2020
- [c15]Anand Srinivasan, Naoya Takeishi:
An MCMC Method for Uncertainty Set Generation via Operator-Theoretic Metrics. CDC 2020: 2714-2719 - [c14]Naoya Takeishi, Yoshinobu Kawahara:
Learning Multiple Nonlinear Dynamical Systems with Side Information. CDC 2020: 3206-3211 - [c13]Naoya Takeishi, Yoshinobu Kawahara:
Knowledge-Based Regularization in Generative Modeling. IJCAI 2020: 2390-2396 - [i10]Naoya Takeishi, Yoshinobu Kawahara:
On Anomaly Interpretation via Shapley Values. CoRR abs/2004.04464 (2020) - [i9]Naoya Takeishi, Yoshinobu Kawahara:
Learning Dynamics Models with Stable Invariant Sets. CoRR abs/2006.08935 (2020) - [i8]Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda:
Policy learning with partial observation and mechanical constraints for multi-person modeling. CoRR abs/2007.03155 (2020)
2010 – 2019
- 2019
- [c12]Naoya Takeishi:
Kernel Learning for Data-Driven Spectral Analysis of Koopman Operators. ACML 2019: 956-971 - [c11]Naoya Takeishi:
Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection. ICDM Workshops 2019: 793-798 - [c10]Riku Sasaki, Naoya Takeishi, Takehisa Yairi, Koichi Hori:
Neural Gray-Box Identification of Nonlinear Partial Differential Equations. PRICAI (2) 2019: 309-321 - [i7]Naoya Takeishi, Yoshinobu Kawahara:
Regularizing Generative Models Using Knowledge of Feature Dependence. CoRR abs/1902.02068 (2019) - [i6]Keisuke Fujii, Naoya Takeishi, Motokazu Hojo, Yuki Inaba, Yoshinobu Kawahara:
Physically-interpretable classification of network dynamics for complex collective motions. CoRR abs/1905.04859 (2019) - [i5]Naoya Takeishi:
Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection. CoRR abs/1909.03495 (2019) - 2018
- [c9]Rem Hida, Naoya Takeishi, Takehisa Yairi, Koichi Hori:
Dynamic and Static Topic Model for Analyzing Time-Series Document Collections. ACL (2) 2018: 516-520 - [c8]Naoya Takeishi, Takehisa Yairi, Yoshinobu Kawahara:
Factorially Switching Dynamic Mode Decomposition for Koopman Analysis of Time-Variant Systems. CDC 2018: 6402-6408 - [i4]Rem Hida, Naoya Takeishi, Takehisa Yairi, Koichi Hori:
Dynamic and Static Topic Model for Analyzing Time-Series Document Collections. CoRR abs/1805.02203 (2018) - [i3]Naoya Takeishi, Kosuke Akimoto:
Knowledge-Based Distant Regularization in Learning Probabilistic Models. CoRR abs/1806.11332 (2018) - 2017
- [j2]Naoya Takeishi, Takehisa Yairi:
Visual Monocular Localization, Mapping, and Motion Estimation of a Rotating Small Celestial Body. J. Robotics Mechatronics 29(5): 856-863 (2017) - [j1]Takehisa Yairi, Naoya Takeishi, Tetsuo Oda, Yuta Nakajima, Naoki Nishimura, Noboru Takata:
A Data-Driven Health Monitoring Method for Satellite Housekeeping Data Based on Probabilistic Clustering and Dimensionality Reduction. IEEE Trans. Aerosp. Electron. Syst. 53(3): 1384-1401 (2017) - [c7]Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi:
Sparse nonnegative dynamic mode decomposition. ICIP 2017: 2682-2686 - [c6]Naoya Takeishi, Yoshinobu Kawahara, Yasuo Tabei, Takehisa Yairi:
Bayesian Dynamic Mode Decomposition. IJCAI 2017: 2814-2821 - [c5]Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi:
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition. NIPS 2017: 1130-1140 - [i2]Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi:
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition. CoRR abs/1710.04340 (2017) - [i1]Chun Fui Liew, Danielle DeLatte, Naoya Takeishi, Takehisa Yairi:
Recent Developments in Aerial Robotics: A Survey and Prototypes Overview. CoRR abs/1711.10085 (2017) - 2016
- [c4]Naoya Takeishi, Takehisa Yairi, Naoki Nishimura, Yuta Nakajima, Noboru Takata:
Dynamic Grouped Mixture Models for Intermittent Multivariate Sensor Data. PAKDD (2) 2016: 221-232 - 2015
- [c3]Naoya Takeishi, Takehisa Yairi, Yuichi Tsuda, Fuyuto Terui, Naoko Ogawa, Yuya Mimasu:
Simultaneous estimation of shape and motion of an asteroid for automatic navigation. ICRA 2015: 2861-2866 - 2014
- [c2]Naoya Takeishi, Takehisa Yairi:
Anomaly detection from multivariate time-series with sparse representation. SMC 2014: 2651-2656 - 2013
- [c1]Akira Tanimoto, Naoya Takeishi, Takehisa Yairi, Yuichi Tsuda, Fuyuto Terui, Naoko Ogawa, Yuya Mimasu:
Fast estimation of asteroid shape and motion for spacecraft navigation. ROBIO 2013: 1550-1555
Coauthor Index
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