This article contains various results on a class of nonmonotone, law-invariant risk functionals
called the signed Choquet integrals. A functional characterization via comonotonic additivity …

We study conditional expectiles, defined as a natural generalisation of conditional
expectations by means of the minimisation of an asymmetric quadratic loss function. We …

We study combinations of risk measures under no restrictive assumption on the set of
alternatives. We develop and discuss results regarding the preservation of properties and …

We propose Choquet regularizers to measure and manage the level of exploration for
reinforcement learning (RL) and reformulate the continuous-time entropy-regularized RL …

There exist uncertain situations in which a random event is not a measurable set, but it is a
point of a linear space inside of which it is possible to study different random quantities …

We propose\emph {Choquet regularizers} to measure and manage the level of exploration
for reinforcement learning (RL), and reformulate the continuous-time entropy-regularized RL …

Let $\rho $ be a general law--invariant convex risk measure, for instance the average value
at risk, and let $ X $ be a financial loss, that is, a real random variable. In practice, either the …

By adopting a distributional viewpoint on law-invariant convex risk measures, we construct
dynamic risk measures (DRMs) at the distributional level. We then apply these DRMs to …

Uncertainty requires suitable techniques for risk assessment. Combining stochastic
approximation and stochastic average approximation, we propose an efficient algorithm to …

Aone-to-one correspondence is drawnbetween lawinvariant risk measures and
divergences, which we define as functionals of pairs of probability measures on arbitrary …