Context. We present a novel approach to the construction of mock galaxy catalogues for large-scale structure analysis based on the distribution of dark matter halos obtained with effective bias models at the field level. Aims. We aim to produce mock galaxy ...
Deep neural networks have completely revolutionized the field of machine
learning by achieving state-of-the-art results on various tasks ranging from
computer vision to protein folding. However, their application is hindered by
their large computational an ...
Anomaly Detection systems based on Machine and Deep learning are the most promising solutions to detect cyberattacks in the industry. However, these techniques are vulnerable to adversarial attacks that downgrade prediction performance. Several techniques ...
Background: Large-scale proteomic studies have to deal with unwanted variability, especially when samples originate from different centers and multiple analytical batches are needed. Such variability is typically added throughout all the steps of a clinica ...
We present the La Mobiliere insurance customers dataset: a 12-year-long longitudinal collection of data on policies of customers of the Swiss insurance company La Mobiliere. To preserve the privacy of La Mobiliere customers, we propose the data aggregated ...
We consider a setup in which confidential i.i.d. samples X1, . . . , Xn from an unknown finite-support distribution p are passed through n copies of a discrete privatization chan- nel (a.k.a. mechanism) producing outputs Y1, . . . , Yn. The channel law gua ...
Sampling has always been at the heart of signal processing providing a bridge between the analogue world and discrete representations of it, as our ability to process data in continuous space is quite limited. Furthermore, sampling plays a key part in unde ...
This paper examines the binning of two types of parts with random characteristics, so that a componentwise monotonic evaluation criterion exhibits a minimum deviation to a given target value over all possible realizations. The optimal matching classes are ...
Popular clustering algorithms based on usual distance functions (e.g., the Euclidean distance) often suffer in high dimension, low sample size (HDLSS) situations, where concentration of pairwise distances and violation of neighborhood structure have advers ...
Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all avai ...
