Hachiya Hirotaka | Department Of Computer Science Tokyo Institute Of Technology

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概要

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著作論文

Statistical active learning for efficient value function approximation in reinforcement learning (ニューロコンピューティング)
Least Absolute Policy Iteration — A Robust Approach to Value Function Approximation
Adaptive importance sampling with automatic model selection in value function approximation (ニューロコンピューティング)
Information-maximization clustering: analytic solution and model selection (情報論的学習理論と機械学習)
New feature selection method for reinforcement learning: conditional mutual information reveals implicit state-reward dependency (情報論的学習理論と機械学習)
Least Absolute Policy Iteration-A Robust Approach to Value Function Approximation
Adaptive importance sampling with automatic model selection in reward weighted regression (ニューロコンピューティング)
Analysis and improvement of policy gradient estimation (情報論的学習理論と機械学習)
Artist agent A[2]: stroke painterly rendering based on reinforcement learning (パターン認識・メディア理解)
Artist agent A[2]: stroke painterly rendering based on reinforcement learning (情報論的学習理論と機械学習)
Modified Newton Approach to Policy Search (情報論的学習理論と機械学習)
Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifier (情報論的学習理論と機械学習)
Relative Density-Ratio Estimation for Robust Distribution Comparison (情報論的学習理論と機械学習)
Modified Newton Approach to Policy Search
Squared-loss Mutual Information Regularization
Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifier
Feature Selection via l_1-Penalized Squared-Loss Mutual Information
Relative Density-Ratio Estimation for Robust Distribution Comparison
Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration (情報論的学習理論と機械学習)
Squared-loss Mutual Information Regularization

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