Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …

In machine learning, if the training data is an unbiased sample of an underlying distribution,
then the learned classification function will make accurate predictions for new samples …

We propose a new scientific application of unsupervised learning techniques to boost our
ability to search for new phenomena in data, by detecting discrepancies between two …

Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on
large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes …

Nearest neighbor (NN) matching is widely used in observational studies for causal effects.
Abadie and Imbens (2006) provided the first large‐sample analysis of NN matching. Their …

Two of the main problems encountered in the development and accurate validation of
photometric redshift (photo-z) techniques are the lack of spectroscopic coverage in the …

Photometric redshift estimation is an indispensable tool of precision cosmology. One
problem that plagues the use of this tool in the era of large-scale sky surveys is that the …

Photo-z estimation: An example of nonparametric conditional density estimation under selection
bias Page 1 The Annals of Applied Statistics 2017, Vol. 11, No. 2, 698–724 DOI …

A probabilistic model is said to be calibrated if its predicted probabilities match the
corresponding empirical frequencies. Calibration is important for uncertainty quantification …

Cultural heritage is a very important element affecting the sustainable development. To
analyze the various forms of spatial management inscribed into sustainable development …