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Found 4 papers, 1 papers with code
Class Probability Matching Using Kernel Methods for Label Shift Adaptation
In covariate shift adaptation where the differences in data distribution arise from variations in feature probabilities, existing approaches naturally address this problem based on \textit{feature probability matching} (\textit{FPM}).
Leveraged Weighted Loss for Partial Label Learning
As an important branch of weakly supervised learning, partial label learning deals with data where each instance is assigned with a set of candidate labels, whereas only one of them is true.
Histogram Transform Ensembles for Large-scale Regression
Instead, we apply kernel histogram transforms (KHT) equipped with smoother regressors such as support vector machines (SVMs), and it turns out that both single and ensemble KHT enjoy almost optimal convergence rates.
Best-scored Random Forest Density Estimation
Thirdly, the convergence rates under $L_{\infty}$-norm is presented.
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