The k‐nearest neighbors algorithm is characterized as a simple yet effective data mining
technique. The main drawback of this technique appears when massive amounts of data …

The European market for pesticides is currently legislated through the well-developed
Regulation (EC) No. 1107/2009. This regulation promotes the competitiveness of European …

A comprehensive and cutting-edge introduction to the foundations and modern applications
of learning theory. Research has exploded in the field of machine learning resulting in …

Many modern methods for prediction leverage nearest neighbor search to find past training
examples most similar to a test example, an idea that dates back in text to at least the 11th …

Estimation of mutual information from observed samples is a basic primitive in machine
learning, useful in several learning tasks including correlation mining, information …

This supplementary material consists of Appendix A, containing a section-by-section
summary of this manuscript in nontechnical language, Appendices B–D, containing …

In transfer learning, we wish to make inference about a target population when we have
access to data both from the distribution itself, and from a different but related source …

Estimating mutual information from independent identically distributed samples drawn from
an unknown joint density function is a basic statistical problem of broad interest with …

Efficient multivariate entropy estimation via k-nearest neighbour distances Page 1 The Annals of
Statistics 2019, Vol. 47, No. 1, 288–318 https://doi.org/10.1214/18-AOS1688 © Institute of …

We study contextual stochastic optimization problems, where we leverage rich auxiliary
observations (eg, product characteristics) to improve decision making with uncertain …