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 …

We propose a new algorithm for model-based distributional reinforcement learning (RL),
and prove that it is minimax-optimal for approximating return distributions in the generative …

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 …

The objective in a traditional reinforcement learning (RL) problem is to find a policy that
optimizes the expected value of a performance metric such as the infinite-horizon cumulative …

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 …

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

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 …

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

The wide availability of data coupled with the computational advances in artificial
intelligence and machine learning promise to enable many future technologies such as …