Bias in datasets can be very detrimental for appropriate statistical estimation. In response to
this problem, importance weighting methods have been developed to match any biased …

We consider the problem of reweighting a source dataset DS to match a target dataset DT,
which plays an important role in dealing with the covariate shift problem. One of the common …

As a promising step, the performance of data analysis and feature learning are able to be
improved if certain pattern matching mechanism is available. One of the feasible solutions …

Many real-world applications exhibit scenarios where distributions represented by training
and test data are not similar, but related by a covariate shift, ie, having equal class …

Various instance weighting methods have been proposed for instance-based transfer
learning. Kernel Mean Matching (KMM) is one of the typical instance weighting approaches …

In many learning problems, the training and testing data follow different distributions and a
particularly common situation is the\textit {covariate shift}. To correct for sampling biases …

The Kernel Mean Matching (KMM) is an elegant algorithm that produces density ratios
between training and test data by minimizing their maximum mean discrepancy in a kernel …

The Kernel Mean Matching (KMM) algorithm is a mathematically rigorous method that
directly weights the training samples such that the mean discrepancy in a kernel space is …

Moment restrictions and their conditional counterparts emerge in many areas of machine
learning and statistics ranging from causal inference to reinforcement learning. Estimators …

Importance weighting is a fundamental procedure in statistics and machine learning that
weights the objective function or probability distribution based on the importance of the …