Contextual bandit algorithms are ubiquitous tools for active sequential experimentation in
healthcare and the tech industry. They involve online learning algorithms that adaptively …

This survey is focused on certain sequential decision-making problems that involve
optimizing over probability functions. We discuss the relevance of these problems for …

When faced with sequential decision-making problems, it is often useful to be able to predict
what would happen if decisions were made using a new policy. Those predictions must …

We study the problem of estimating the distribution of the return of a policy using an offline
dataset that is not generated from the policy, ie, distributional offline policy evaluation (OPE) …

Most off-policy evaluation methods for contextual bandits have focused on the expected
outcome of a policy, which is estimated via methods that at best provide only asymptotic …

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

Recommender systems are more and more often modelled as repeated decision making
processes–deciding which (ranking of) items to recommend to a given user. Each decision …

Motivated by the fragility of neural network (NN) controllers in safety-critical applications, we
present a data-driven framework for verifying the risk of stochastic dynamical systems with …

Tipping points are abrupt, drastic, and often irreversible changes in the evolution of non-
stationary and chaotic dynamical systems. For instance, increased greenhouse gas …

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 …