An overview of recent efforts in the machine learning community to deal with dataset and
covariate shift, which occurs when test and training inputs and outputs have different …

Theory, algorithms, and applications of machine learning techniques to overcome" covariate
shift" non-stationarity. As the power of computing has grown over the past few decades, the …

A common assumption in supervised learning is that the input points in the training set follow
the same probability distribution as the input points that will be given in the future test phase …

Logistic regression is by far the most widely used classifier in real-world applications. In this
paper, we benchmark the state-of-the-art active learning methods for logistic regression and …

We propose a new statistical approach to the problem of inlier-based outlier detection, ie,
finding outliers in the test set based on the training set consisting only of inliers. Our key idea …

Abstract Generative Adversarial Networks (GANs) have been successfully applied to the
problem of policy imitation in a model-free setup. However, the computation graph of GANs …

A common assumption in supervised learning is that the training and test input points follow
the same probability distribution. However, this assumption is not fulfilled, eg, in …

Methods for directly estimating the ratio of two probability density functions have been
actively explored recently since they can be used for various data processing tasks such as …

Covariate shift is a situation in supervised learning where training and test inputs follow
different distributions even though the functional relation remains unchanged. A common …

The goal of active learning is to determine the locations of training input points so that the
generalization error is minimized. We discuss the problem of active learning in linear …