We introduce the weighted likelihood bootstrap (WLB) as a way to simulate approximately
from a posterior distribution. This method is often easy to implement, requiring only an …

Innovation and standardization in 5G have brought advancements to every facet of the
cellular architecture. This ranges from the introduction of new frequency bands and …

We study the stochastic multi-armed bandits problem in the presence of adversarial
corruption. We present a new algorithm for this problem whose regret is nearly optimal …

In reward-poisoning attacks against reinforcement learning (RL), an attacker can perturb the
environment reward $ r_t $ into $ r_t+\delta_t $ at each step, with the goal of forcing the RL …

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 black-box reward poisoning attacks against reinforcement learning (RL), in which
an adversary aims to manipulate the rewards to mislead a sequence of RL agents with …

The cooperative bandit problem is increasingly becoming relevant due to its applications in
large-scale decision-making. However, most research for this problem focuses exclusively …

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 …

In this work we revisit two classic high-dimensional online learning problems, namely linear
regression and contextual bandits, from the perspective of adversarial robustness. Existing …

We study bandit best-arm identification with arbitrary and potentially adversarial rewards. A
simple random uniform learner obtains the optimal rate of error in the adversarial scenario …