Output Divergence Criterion for Active Learning in Collaborative Settings

スポンサーリンク

概要

• 論文の詳細を見る

• We address the task of active learning for linear regression models in collaborative settings. The goal of active learning is to select training points that would allow accurate prediction of output values. We propose a new active learning criterion that is aimed at directly improving the accuracy of the output value estimation by analyzing the effect of the new training points on the estimates of the output values. The advantages of the proposed method are highlighted in collaborative settings, in which most of the data points are missing, and the number of training data points is much smaller than the number of the parameters of the model.

• 一般社団法人 情報処理学会の論文

著者

• TOMIOKA Ryota

  Department of Mathematical Informatics, The University of Tokyo

• Tomioka Ryota

  Department Of Computer Science Tokyo Institute Of Technology

• Sugiyama Masashi

  Department of Applied Chemistry, Yamanashi University

• Rubens Neil

  Graduate School of Information Systems, University of Electro-Communications

関連論文

• Statistical active learning for efficient value function approximation in reinforcement learning (ニューロコンピューティング)
• Improving the Accuracy of Least-Squares Probabilistic Classifiers
• Improving the Accuracy of Least-Squares Probabilistic Classifiers
• Approximating the Best Linear Unbiased Estimator of Non-Gaussian Signals with Gaussian Noise
• Regularization Strategies and Empirical Bayesian Learning for MKL
• Adaptive importance sampling with automatic model selection in value function approximation (ニューロコンピューティング)
• Recent Advances and Trends in Large-Scale Kernel Methods
• Syntheses of New Artificial Zinc Finger Proteins Containing Trisbipyridine-ruthenium Amino Acid at The N-or C-terminus as Fluorescent Probes
• Analytic Optimization of Shrinkage Parameters Based on Regularized Subspace Information Criterion(Neural Networks and Bioengineering)
• Constructing Kernel Functions for Binary Regression(Pattern Recognition)
• Optimal design of regularization term and regularization parameter by subspace information criterion
• Information-maximization clustering: analytic solution and model selection (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)
• SERAPH: semi-supervised metric learning paradigm with hyper sparsity (情報論的学習理論と機械学習)
• Analysis and improvement of policy gradient estimation (情報論的学習理論と機械学習)
• Output Divergence Criterion for Active Learning in Collaborative Settings
• Output divergence criterion for active learning in collaborative settings (数理モデル化と問題解決・バイオ情報学)
• Dependence minimizing regression with model selection for non-linear causal inference under non-Gaussian noise (情報論的学習理論と機械学習)
• Canonical dependency analysis based on squared-loss mutual information (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)
• Relative Density-Ratio Estimation for Robust Distribution Comparison (情報論的学習理論と機械学習)
• Squared-loss Mutual Information Regularization
• Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifier
• Winning the Kaggle Algorithmic Trading Challenge with the Composition of Many Models and Feature Engineering (情報論的学習理論と機械学習)
• Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation
• Early Stopping Heuristics in Pool-Based Incremental Active Learning for Least-Squares Probabilistic Classifier (情報論的学習理論と機械学習)
• Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration (情報論的学習理論と機械学習)
• Output Divergence Criterion for Active Learning in Collaborative Settings
• Output Divergence Criterion for Active Learning in Collaborative Settings
• Photochromism of benzylviologens containing methyl groups on pyridinium rings and embedded in solid poly(N-vinyl-2-pyrrolidone) matrix.
• Clustering Unclustered Data : Unsupervised Binary Labeling of Two Datasets Having Different Class Balances
• Direct Approximation of Quadratic Mutual Information and Its Application to Dependence-Maximization Clustering
• Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
• Squared-loss Mutual Information Regularization
• Early Stopping Heuristics in Pool-Based Incremental Active Learning for Least-Squares Probabilistic Classifier
• Winning the Kaggle Algorithmic Trading Challenge with the Composition of Many Models and Feature Engineering
• Improving Importance Estimation in Pool-based Batch Active Learning for Approximate Linear Regression

もっと見る 閉じる

スポンサーリンク