H. Hachiya

发表

Value Function Approximation on Non-Linear Manifolds for Robot Motor Control

Masashi Sugiyama, Sethu Vijayakumar, Hirotaka Hachiya, 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

Geodesic Gaussian kernels for value function approximation

Masashi Sugiyama, Sethu Vijayakumar, Hirotaka Hachiya, 2008, Auton. Robots.

Adaptive importance sampling for value function approximation in off-policy reinforcement learning

Masashi Sugiyama, Jan Peters, Hirotaka Hachiya, 2009, Neural Networks.

Parametric Return Density Estimation for Reinforcement Learning pdf

Masashi Sugiyama, Hisashi Kashima, Toshiyuki Tanaka, 2010, UAI.

Nonparametric Return Distribution Approximation for Reinforcement Learning

Masashi Sugiyama, Hisashi Kashima, Toshiyuki Tanaka, 2010, ICML.

Active Policy Iteration: Efficient Exploration through Active Learning for Value Function Approximation in Reinforcement Learning

Masashi Sugiyama, Hirotaka Hachiya, Takayuki Akiyama, 2009, IJCAI.

Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting

Masashi Sugiyama, Hirotaka Hachiya, Ning Xie, 2012, ICML.

Least-squares Conditional Density Estimation Masashi Sugiyama

Masashi Sugiyama, Taiji Suzuki, I. Takeuchi, 2009 .

Adaptive importance sampling with automatic model selection in reward weighted regression (ニューロコンピューティング)

Masashi Sugiyama, Jan Peters, Hirotaka Hachiya, 2009 .

Reward Weighted Regression with Sample Reuse

Masashi Sugiyama, Jan Peters, Hirotaka Hachiya, 2011 .

Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifiers

Masashi Sugiyama, Hirotaka Hachiya, Hyun Ha Nam, 2013, IEICE Trans. Inf. Syst..

Improving Model-based Reinforcement Learning with Multitask Learning (数理モデル化と問題解決(MPS) Vol.2009-MPS-76)

Masashi Sugiyama, Hirotaka Hachiya, Jaak Simm, 2010 .

Information-Maximization Clustering Based on Squared-Loss Mutual Information

Masashi Sugiyama, H. Hachiya, M. Yamada, 2011, Neural Computation.

Robot Control by Least-Squares Policy Iteration with Geodesic Gaussian Kernels

Masashi Sugiyama, H. Hachiya, 2007 .

Study on Echocardiographic Image Segmentation Based on Attention U-Net

H. Hachiya, Haiyuan Wu, Jiwei Zhang, 2022, 2022 IEEE International Conference on Mechatronics and Automation (ICMA).

Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration

Jun Morimoto, Masashi Sugiyama, Voot Tangkaratt, 2012, Neural Computation.

Feature Selection via L1-Penalized Squared-Loss Mutual Information pdf

Masashi Sugiyama, Wittawat Jitkrittum, Hirotaka Hachiya, 2012, IEICE Trans. Inf. Syst..

Least-Squares Conditional Density Estimation

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2010, IEICE Trans. Inf. Syst..

Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning

Gang Niu, Bo Dai, Masashi Sugiyama, 2013, ICML.

Squared-loss Mutual Information Regularization (情報論的学習理論と機械学習)

Gang Niu, Bo Dai, Masashi Sugiyama, 2012 .

Relative Density-Ratio Estimation for Robust Distribution Comparison

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2011, Neural Computation.

Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition

Masashi Sugiyama, Naonori Ueda, Hirotaka Hachiya, 2012, Neurocomputing.

Simulation of broad-band ground motions with consistent long-period and short-period components using the Wasserstein interpolation of acceleration envelopes

H. Hachiya, N. Ueda, H. Fujiwara, 2021, Geophysical Journal International.

Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning

K. Fukumizu, H. Hachiya, Yuki Saito, 2020, ECCV.

Analysis and Improvement of Policy Gradient Estimation

Gang Niu, Masashi Sugiyama, Tingting Zhao, 2011, NIPS.

Conditional Density Estimation via Least-Squares Density Ratio Estimation

Takafumi Kanamori, Masashi Sugiyama, Taiji Suzuki, 2010, AISTATS.

Efficient data reuse in value function approximation

Masashi Sugiyama, Jan Peters, Hirotaka Hachiya, 2009, 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning.

Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation

Masashi Sugiyama, Jan Peters, Hirotaka Hachiya, 2007, AAAI.

Distance estimation with 2.5D anchors and its application to robot navigation

H. Hachiya, Takayuki Nakamura, Yuki Saito, 2018, ROBOMECH Journal.

Deep Set-to-Set Matching and Learning pdf

Kenji Fukumizu, Hirotaka Hachiya, Yuki Saito, 2019, ArXiv.

Combining Static Specular Flow and Highlight with Deep Features for Specular Surface Detection

H. Hachiya, Yuto Yoshimura, 2023, 2023 18th International Conference on Machine Vision and Applications (MVA).

Least Absolute Policy Iteration-A Robust Approach to Value Function Approximation

Masashi Sugiyama, Hisashi Kashima, Tetsuro Morimura, 2010, IEICE Trans. Inf. Syst..

Least absolute policy iteration for robust value function approximation

Masashi Sugiyama, Hisashi Kashima, Tetsuro Morimura, 2009, 2009 IEEE International Conference on Robotics and Automation.