Learning under nonstationarity: covariate shift and class-balance change

Cites Work

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Direct importance estimation for covariate shift adaptation
Statistical analysis of kernel-based least-squares density-ratio estimation
Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search
Semi-supervised speaker identification under covariate shift
Two-sample test statistics for measuring discrepancies between two multivariate probability density functions using kernel-based density estimates
Active learning algorithm using the maximum weighted log-likelihood estimator
On the criterion, that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Semiparametric density estimation under a two-sample density ratio model
Adaptive importance sampling for value function approximation in off-policy reinforcement learning
Dimensionality reduction for density ratio estimation in high-dimensional spaces
Efficient exploration through active learning for value function approximation in reinforcement learning
Nonparametric and semiparametric models.
Density-ratio matching under the Bregman divergence: a unified framework of density-ratio estimation
A batch ensemble approach to active learning with model selection
Computational complexity of kernel-based density-ratio estimation: a condition number analysis
Pool-based active learning in approximate linear regression
Robust weights and designs for biased regression models: Least squares and generalized \(M\)-estimation
Subspace Information Criterion for Model Selection
Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure
Density Ratio Estimation in Machine Learning
Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning
Estimating density ratio with application to discriminant analysis
Input-dependent estimation of generalization error under covariate shift
On nonparametric discrimination using density differences
Inferences for case-control and semiparametric two-sample density ratio models
Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization
Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration
Relative Density-Ratio Estimation for Robust Distribution Comparison
Density-Difference Estimation
On kernel estimators of density ratio
On Information and Sufficiency
The elements of statistical learning. Data mining, inference, and prediction
A new look at the statistical model identification

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