The multi-armed bandit (MAB) problem is a classical learning task that exemplifies the
exploration-exploitation tradeoff. However, standard formulations do not take into account …

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 …

Classical multi-armed bandit problems use the expected value of an arm as a metric to
evaluate its goodness. However, the expected value is a risk-neutral metric. In many …

We introduce the safe linear stochastic bandit framework—a generalization of linear
stochastic bandits—where, in each stage, the learner is required to select an arm with an …

We study multi-armed bandit problems that use conditional value-at-risk as an underlying
risk measure. In particular, we propose a new upper confidence bound algorithm and …

We consider a variant of the best arm identification task in stochastic multi-armed bandits.
Motivated by risk-averse decision-making problems, our goal is to identify a set of $ m …

The classical multi-armed bandit (MAB) framework studies the exploration-exploitation
dilemma of the decisionmaking problem and always treats the arm with the highest expected …

We design and analyze Variance-Aware-Lower and Upper Confidence Bound (VA-LUCB), a
parameter-free algorithm, for identifying the best arm under the fixed-confidence setup and …

In several applications such as clinical trials and financial portfolio optimization, the
expected value (or the average reward) does not satisfactorily capture the merits of a drug or …

Motivated by concerns about making online decisions that incur undue amount of risk at
each time step, in this paper, we formulate the probably anytime-safe stochastic …