Recent years have seen an unprecedented spread of Unmanned Aerial Vehicles (UAVs, or
“drones”), which are highly useful for both civilian and military applications. Flight safety is a …

The increasing size of data generated by smartphones and IoT devices motivated the
development of Federated Learning (FL), a framework for on-device collaborative training of …

Learning causal effects from data is a fundamental problem across the sciences.
Determining the identifiability of a target effect from a combination of the observational …

Within the scaling laws paradigm, which underpins the training of large neural networks like
ChatGPT and Llama, we consider a supervised regression setting and establish the …

When dealing with imbalanced classification data, reweighting the loss function is a
standard procedure allowing to equilibrate between the true positive and true negative rates …

Active Learning (AL) aims to optimize basic learned model (s) iteratively by selecting and
annotating unlabeled data samples that are deemed to best maximise the model …

Positive-Unlabeled learning (PU learning) is a special case of semi-supervised binary
classification where only a fraction of positive examples is labeled. The challenge is then to …

While machine learning algorithms hold promise for personalised medicine, their clinical
adoption remains limited. One critical factor contributing to this restraint is sample selection …

Condensing large datasets into smaller synthetic counterparts has demonstrated its promise
for image classification. However, previous research has overlooked a crucial concern in …

Covariate shift may impact the operational safety performance of neural networks. A re-
evaluation of the safety performance, however, requires collecting new operational data and …