Detecting out-of-distribution (OOD) data is crucial for robust machine learning systems.
Normalizing flows are flexible deep generative models that often surprisingly fail to …

Multiple instance learning (MIL) is a form of weakly supervised learning where training
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …

Autoregressive models are among the best performing neural density estimators. We
describe an approach for increasing the flexibility of an autoregressive model, based on …

Several techniques for domain adaptation have been proposed to account for differences in
the distribution of the data used for training and testing. The majority of this work focuses on …

With the recent explosion of big data, real-world data are increasingly being affected by
larger degrees of class imbalance, likely hindering Machine Learning algorithm …

The subtle and unique imprint of dark matter substructure on extended arcs in strong-lensing
systems contains a wealth of information about the properties and distribution of dark matter …

In many fields of science, generalized likelihood ratio tests are established tools for
statistical inference. At the same time, it has become increasingly common that a simulator …

Artificial intelligence, and particularly machine learning, has been used in many ways by the
research community to turn a variety of diverse and even heterogeneous data sources into …

A bstract The unsupervised search for overdense regions in high-dimensional feature
spaces, where locally high population densities may be associated with anomalous …

Many applications require learning classifiers or regressors that are both accurate and
cheap to evaluate. Prediction cost can be drastically reduced if the learned predictor is …