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ORCIDHirotaka Hachiya
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2020 – today
- 2024
[j19]Hirotaka Hachiya
, Yuki Saito:
Set representative vector and its asymmetric attention-based transformation for heterogeneous set-to-set matching. Neurocomputing 578: 127372 (2024)- [j18]Hirotaka Hachiya, Yuto Yoshimura:
Specular Surface Detection with Deep Static Specular Flow and Highlight. Mach. Vis. Appl. 35(6): 126 (2024) - 2023
- [j17]Jiwei Zhang, Haiyuan Wu, Qian Chen, Hirotaka Hachiya:
Multi-feature subspace representation network for person re-identification via bird's-eye view image. Comput. Animat. Virtual Worlds 34(6) (2023) - [j16]Hirotaka Hachiya, Sujun Hong:
Multistream-Based Marked Point Process With Decomposed Cumulative Hazard Functions. Neural Comput. 35(4): 699-726 (2023) - [c26]Yuhei Nomura, Hirotaka Hachiya:
Frequency-dependent Image Reconstruction Error for Micro Defect Detection. ACML 2023: 1007-1022 - [c25]Hirotaka Hachiya, Yuto Yoshimura:
Combining Static Specular Flow and Highlight with Deep Features for Specular Surface Detection. MVA 2023: 1-5 - 2022
- [c24]Hirotaka Hachiya, Kotaro Nagayoshi, Asako Iwaki, Takahiro Maeda, Naonori Ueda, Hiroyuki Fujiwara:
Position-dependent partial convolutions for supervised spatial interpolation. ACML 2022: 420-435 - 2021
- [c23]Hirotaka Hachiya, Yusuke Masumoto, Yuki Mori, Naonori Ueda:
Encoder-decoder-based image transformation approach for integrating precipitation forecasts. ACML 2021: 174-188 - [c22]Sujun Hong, Hirotaka Hachiya:
Multi-stream based marked point process. ACML 2021: 1269-1284 - 2020
- [c21]Yuki Saito, Takuma Nakamura, Hirotaka Hachiya, Kenji Fukumizu:
Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning. ECCV (17) 2020: 626-646 - [i6]Tsuyoshi Okita, Hirotaka Hachiya, Sozo Inoue, Naonori Ueda:
Translation Between Waves, wave2wave. CoRR abs/2007.10394 (2020)
2010 – 2019
- 2019
- [c20]Hirotaka Hachiya, Yu Yamamoto, Kazuro Hirahara, Naonori Ueda:
Adaptive truncated residual regression for fine-grained regression problems. ACML 2019: 868-882 - [i5]Yuki Saito, Takuma Nakamura, Hirotaka Hachiya, Kenji Fukumizu:
Deep Set-to-Set Matching and Learning. CoRR abs/1910.09972 (2019) - 2018
- [c19]Shohei Wakita, Takayuki Nakamura, Hirotaka Hachiya:
Laser Variational Autoencoder for Map Construction and Self-Localization. SMC 2018: 3993-3998 - [c18]Hirotaka Hachiya, Yuki Saito, Kazuma Iteya, Masaya Nomura, Takayuki Nakamura:
2.5D Faster R-CNN for Distance Estimation. SMC 2018: 3999-4004 - 2014
- [j15]Masashi Sugiyama, Gang Niu, Makoto Yamada, Manabu Kimura, Hirotaka Hachiya:
Information-Maximization Clustering Based on Squared-Loss Mutual Information. Neural Comput. 26(1): 84-131 (2014) - 2013
- [j14]Ning Xie, Hirotaka Hachiya, Masashi Sugiyama:
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting. IEICE Trans. Inf. Syst. 96-D(5): 1134-1144 (2013) - [j13]Wittawat Jitkrittum, Hirotaka Hachiya, Masashi Sugiyama:
Feature Selection via l1-Penalized Squared-Loss Mutual Information. IEICE Trans. Inf. Syst. 96-D(7): 1513-1524 (2013) - [j12]Hyun Ha Nam, Hirotaka Hachiya, Masashi Sugiyama:
Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifiers. IEICE Trans. Inf. Syst. 96-D(8): 1871-1874 (2013) - [j11]Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama:
Relative Density-Ratio Estimation for Robust Distribution Comparison. Neural Comput. 25(5): 1324-1370 (2013) - [j10]Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama:
Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration. Neural Comput. 25(6): 1512-1547 (2013) - [c17]Hirotaka Hachiya, Masakazu Matsugu:
NSH: Normality Sensitive Hashing for Anomaly Detection. ICCV Workshops 2013: 795-802 - [c16]Gang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama:
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning. ICML (3) 2013: 10-18 - [i4]Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama:
Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration. CoRR abs/1301.3966 (2013) - 2012
- [j9]Jaak Simm, Masashi Sugiyama, Hirotaka Hachiya:
Multi-Task Approach to Reinforcement Learning for Factored-State Markov Decision Problems. IEICE Trans. Inf. Syst. 95-D(10): 2426-2437 (2012) - [j8]Hirotaka Hachiya, Masashi Sugiyama, Naonori Ueda:
Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition. Neurocomputing 80: 93-101 (2012) - [j7]Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama:
Analysis and improvement of policy gradient estimation. Neural Networks 26: 118-129 (2012) - [c15]Hyun Ha Nam, Hirotaka Hachiya, Masashi Sugiyama:
Computationally efficient multi-label classification by least-squares probabilistic classifier. ICASSP 2012: 2077-2080 - [c14]Ning Xie, Hirotaka Hachiya, Masashi Sugiyama:
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting. ICML 2012 - [i3]Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka:
Parametric Return Density Estimation for Reinforcement Learning. CoRR abs/1203.3497 (2012) - [i2]Ning Xie, Hirotaka Hachiya, Masashi Sugiyama:
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting. CoRR abs/1206.4634 (2012) - [i1]Wittawat Jitkrittum, Hirotaka Hachiya, Masashi Sugiyama:
Feature Selection via L1-Penalized Squared-Loss Mutual Information. CoRR abs/1210.1960 (2012) - 2011
- [j6]Hirotaka Hachiya, Jan Peters, Masashi Sugiyama:
Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning. Neural Comput. 23(11): 2798-2832 (2011) - [c13]Masashi Sugiyama, Makoto Yamada, Manabu Kimura, Hirotaka Hachiya:
On Information-Maximization Clustering: Tuning Parameter Selection and Analytic Solution. ICML 2011: 65-72 - [c12]Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama:
Analysis and Improvement of Policy Gradient Estimation. NIPS 2011: 262-270 - [c11]Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama:
Relative Density-Ratio Estimation for Robust Distribution Comparison. NIPS 2011: 594-602 - 2010
- [j5]Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara:
Least-Squares Conditional Density Estimation. IEICE Trans. Inf. Syst. 93-D(3): 583-594 (2010) - [j4]Masashi Sugiyama, Hirotaka Hachiya, Hisashi Kashima, Tetsuro Morimura:
Least Absolute Policy Iteration-A Robust Approach to Value Function Approximation. IEICE Trans. Inf. Syst. 93-D(9): 2555-2565 (2010) - [j3]Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiyama:
Efficient exploration through active learning for value function approximation in reinforcement learning. Neural Networks 23(5): 639-648 (2010) - [c10]Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka:
Nonparametric Return Distribution Approximation for Reinforcement Learning. ICML 2010: 799-806 - [c9]Hirotaka Hachiya, Masashi Sugiyama:
Feature Selection for Reinforcement Learning: Evaluating Implicit State-Reward Dependency via Conditional Mutual Information. ECML/PKDD (1) 2010: 474-489 - [c8]Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka:
Parametric Return Density Estimation for Reinforcement Learning. UAI 2010: 368-375 - [c7]Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara:
Conditional Density Estimation via Least-Squares Density Ratio Estimation. AISTATS 2010: 781-788
2000 – 2009
- 2009
- [j2]Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama, Jan Peters:
Adaptive importance sampling for value function approximation in off-policy reinforcement learning. Neural Networks 22(10): 1399-1410 (2009) - [c6]Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama, Jan Peters:
Efficient data reuse in value function approximation. ADPRL 2009: 8-15 - [c5]Masashi Sugiyama, Hirotaka Hachiya, Hisashi Kashima, Tetsuro Morimura:
Least absolute policy iteration for robust value function approximation. ICRA 2009: 2904-2909 - [c4]Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiyama:
Active Policy Iteration: Efficient Exploration through Active Learning for Value Function Approximation in Reinforcement Learning. IJCAI 2009: 980-985 - [c3]Hirotaka Hachiya, Jan Peters, Masashi Sugiyama:
Efficient Sample Reuse in EM-Based Policy Search. ECML/PKDD (1) 2009: 469-484 - 2008
- [j1]Masashi Sugiyama, Hirotaka Hachiya, Christopher Towell, Sethu Vijayakumar:
Geodesic Gaussian kernels for value function approximation. Auton. Robots 25(3): 287-304 (2008) - [c2]Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama, Jan Peters:
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation. AAAI 2008: 1351-1356 - 2007
- [c1]Masashi Sugiyama, Hirotaka Hachiya, Christopher Towell, Sethu Vijayakumar:
Value Function Approximation on Non-Linear Manifolds for Robot Motor Control. ICRA 2007: 1733-1740
Coauthor Index
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