Uncertainty is prevalent in engineering design and data-driven problems and, more broadly,
in decision making. Due to inherent risk-averseness and ambiguity about assumptions, it is …

Abstract Conditional Value-at-Risk (CVaR) is a widely used risk metric in applications such
as finance. We derive concentration bounds for CVaR estimates, considering separately the …

Standard uniform convergence results bound the generalization gap of the expected loss
over a hypothesis class. The emergence of risk-sensitive learning requires generalization …

Spectral risk objectives–also called L-risks–allow for learning systems to interpolate
between optimizing average-case performance (as in empirical risk minimization) and worst …

In forming learning objectives, one oftentimes needs to aggregate a set of individual values
to a single output. Such cases occur in the aggregate loss, which combines individual losses …

In forming learning objectives, one oftentimes needs to aggregate a set of individual values
to a single output. Such cases occur in the aggregate loss, which combines individual losses …

Abstract Conditional Value at Risk ($\textsc {CVaR} $) is a``coherent risk measure''which
generalizes expectation (reduced to a boundary parameter setting). Widely used in …

Uncertainty is prevalent in engineering design, statistical learning, and decision making
broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to …

We study finite episodic Markov decision processes incorporating dynamic risk measures to
capture risk sensitivity. To this end, we present two model-based algorithms applied to\emph …

We present a framework to interpret signal temporal logic (STL) formulas over discrete-time
stochastic processes in terms of the induced risk. Each realization of a stochastic process …