Masashi Sugiyama

TL;DR: This volume offers an overview of current efforts to deal with dataset and covariate shift, and places dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning.

TL;DR: A new linear supervised dimensionality reduction method called local Fisher discriminant analysis (LFDA), which effectively combines the ideas of FDA and LPP, and which can be easily computed just by solving a generalized eigenvalue problem.

TL;DR: Co-teaching as discussed by the authors trains two deep neural networks simultaneously, and let them teach each other given every mini-batch: first, each network feeds forward all data and selects some data of possibly clean labels; secondly, two networks communicate with each other what data in this minibatch should be used for training; finally, each networks back propagates the data selected by its peer network and updates itself.

TL;DR: This paper proposes a new method called importance weighted cross validation (IWCV), for which its unbiasedness even under the covariate shift is proved, and the IWCV procedure is the only one that can be applied for unbiased classification under covariates.

TL;DR: This paper proposes a direct importance estimation method that does not involve density estimation and is equipped with a natural cross validation procedure and hence tuning parameters such as the kernel width can be objectively optimized.