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Hirotaka Hachiya

Showing results (1-10 of 8) with videos related to

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Neural Computation|February 24, 2023
Multistream-Based Marked Point Process With Decomposed Cumulative Hazard FunctionsHirotaka Hachiya, Sujun Hong
Neural Computation|August 20, 2011
Reward-weighted regression with sample reuse for direct policy search in reinforcement learningHirotaka Hachiya, Jan Peters, Masashi Sugiyama
Neural Networks : the Official Journal of the International Neural Network Society|January 19, 2010
Efficient exploration through active learning for value function approximation in reinforcement learningTakayuki Akiyama, Hirotaka Hachiya, Masashi Sugiyama
Neural Networks : the Official Journal of the International Neural Network Society|October 25, 2011
Analysis and improvement of policy gradient estimationTingting Zhao, Hirotaka Hachiya, Gang Niu, et al.
Neural Networks : the Official Journal of the International Neural Network Society|February 14, 2009
Adaptive importance sampling for value function approximation in off-policy reinforcement learningHirotaka Hachiya, Takayuki Akiyama, Masashi Sugiayma, et al.
Neural Computation|March 23, 2013
Efficient sample reuse in policy gradients with parameter-based explorationTingting Zhao, Hirotaka Hachiya, Voot Tangkaratt, et al.
Neural Computation|April 4, 2013
Relative density-ratio estimation for robust distribution comparisonMakoto Yamada, Taiji Suzuki, Takafumi Kanamori, et al.
Neural Computation|October 10, 2013
Information-maximization clustering based on squared-loss mutual informationMasashi Sugiyama, Gang Niu, Makoto Yamada, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Neural Computation|February 24, 2023
Multistream-Based Marked Point Process With Decomposed Cumulative Hazard FunctionsHirotaka Hachiya, Sujun Hong
Neural Computation|August 20, 2011
Reward-weighted regression with sample reuse for direct policy search in reinforcement learningHirotaka Hachiya, Jan Peters, Masashi Sugiyama
Neural Networks : the Official Journal of the International Neural Network Society|January 19, 2010
Efficient exploration through active learning for value function approximation in reinforcement learningTakayuki Akiyama, Hirotaka Hachiya, Masashi Sugiyama
Neural Networks : the Official Journal of the International Neural Network Society|October 25, 2011
Analysis and improvement of policy gradient estimationTingting Zhao, Hirotaka Hachiya, Gang Niu, et al.
Neural Networks : the Official Journal of the International Neural Network Society|February 14, 2009
Adaptive importance sampling for value function approximation in off-policy reinforcement learningHirotaka Hachiya, Takayuki Akiyama, Masashi Sugiayma, et al.
Neural Computation|March 23, 2013
Efficient sample reuse in policy gradients with parameter-based explorationTingting Zhao, Hirotaka Hachiya, Voot Tangkaratt, et al.
Neural Computation|April 4, 2013
Relative density-ratio estimation for robust distribution comparisonMakoto Yamada, Taiji Suzuki, Takafumi Kanamori, et al.
Neural Computation|October 10, 2013
Information-maximization clustering based on squared-loss mutual informationMasashi Sugiyama, Gang Niu, Makoto Yamada, et al.
Pageof 1