Voot Tangkaratt

38 papers A* 4A 2C 2Journal 27Unranked 3

Year	Rank	Type	Title / Venue / Authors
2022	J	`jnl`	Discovering diverse solutions in deep reinforcement learning by maximizing state-action-based mutual information. Neural Networks Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
2021	J	`jnl`	Discovering Diverse Solutions in Deep Reinforcement Learning. CoRR Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
2021	C	`conf`	Meta-Model-Based Meta-Policy Optimization. ACML Takuya Hiraoka, Takahisa Imagawa, Voot Tangkaratt, Takayuki Osa, Takashi Onishi, Yoshimasa Tsuruoka
2021	A	`conf`	Robust Imitation Learning from Noisy Demonstrations. AISTATS Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama
2020	J	`jnl`	Active deep Q-learning with demonstration. Mach. Learn. Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama
2020	J	`jnl`	Meta-Model-Based Meta-Policy Optimization. CoRR Takuya Hiraoka, Takahisa Imagawa, Voot Tangkaratt, Takayuki Osa, Takashi Onishi, Yoshimasa Tsuruoka
2020	J	`jnl`	Robust Imitation Learning from Noisy Demonstrations. CoRR Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama
2020	A	`conf`	Simultaneous Planning for Item Picking and Placing by Deep Reinforcement Learning. IROS Tatsuya Tanaka, Toshimitsu Kaneko, Masahiro Sekine, Voot Tangkaratt, Masashi Sugiyama
2020	A*	`conf`	Variational Imitation Learning with Diverse-quality Demonstrations. ICML Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama
2019	—	`conf`	Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization. ICLR (Poster) Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
2019	J	`jnl`	Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization. CoRR Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
2019	A*	`conf`	Imitation Learning from Imperfect Demonstration. ICML Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama
2019	J	`jnl`	Imitation Learning from Imperfect Demonstration. CoRR Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama
2019	J	`jnl`	TD-regularized actor-critic methods. Mach. Learn. Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan
2019	J	`jnl`	VILD: Variational Imitation Learning with Diverse-quality Demonstrations. CoRR Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama
2018	J	`jnl`	Active Deep Q-learning with Demonstration. CoRR Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama
2018	A*	`conf`	Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. ICML Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava
2018	J	`jnl`	Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. CoRR Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava
2018	—	`conf`	Guide Actor-Critic for Continuous Control. ICLR (Poster) Voot Tangkaratt, Abbas Abdolmaleki, Masashi Sugiyama
2018	J	`jnl`	Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. Neural Comput. Hiroaki Sasaki, Voot Tangkaratt, Gang Niu, Masashi Sugiyama
2018	J	`jnl`	TD-Regularized Actor-Critic Methods. CoRR Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan
2017	J	`jnl`	Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction. Neural Comput. Voot Tangkaratt, Hiroaki Sasaki, Masashi Sugiyama
2017	A*	`conf`	Policy Search with High-Dimensional Context Variables. AAAI Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama
2017	J	`jnl`	Variational Adaptive-Newton Method for Explorative Learning. CoRR Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt, Zuozhu Liu, Didrik Nielsen
2017	J	`jnl`	Vprop: Variational Inference using RMSprop. CoRR Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt, Yarin Gal
2016	J	`jnl`	Model-based reinforcement learning with dimension reduction. Neural Networks Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama
2016	J	`jnl`	Policy Search with High-Dimensional Context Variables. CoRR Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama
2016	J	`jnl`	Trial and Error: Using Previous Experiences as Simulation Models in Humanoid Motor Learning. IEEE Robotics Autom. Mag. Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto
2015	J	`jnl`	Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization. Neural Comput. Voot Tangkaratt, Ning Xie, Masashi Sugiyama
2015	J	`jnl`	Direct conditional probability density estimation with sparse feature selection. Mach. Learn. Motoki Shiga, Voot Tangkaratt, Masashi Sugiyama
2015	C	`conf`	Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. ACML Hiroaki Sasaki, Voot Tangkaratt, Masashi Sugiyama
2014	J	`jnl`	Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization. CoRR Voot Tangkaratt, Ning Xie, Masashi Sugiyama
2014	J	`jnl`	Efficient Reuse of Previous Experiences to Improve Policies in Real Environment. CoRR Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto
2014	—	`conf`	Efficient reuse of previous experiences in humanoid motor learning. Humanoids Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto
2014	J	`jnl`	Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation. Neural Networks Voot Tangkaratt, Syogo Mori, Tingting Zhao, Jun Morimoto, Masashi Sugiyama
2013	J	`jnl`	Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration. Neural Comput. Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama
2013	J	`jnl`	Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration CoRR Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama
2013	J	`jnl`	Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation. CoRR Syogo Mori, Voot Tangkaratt, Tingting Zhao, Jun Morimoto, Masashi Sugiyama