Organizational dependency on Information and Communication Technology (ICT) drives the
preparedness challenge to cope with business process disruptions. Business Continuity …

Traditional multi-armed bandit (MAB) frameworks, predominantly examined under stochastic
or adversarial settings, often overlook the temporal dynamics inherent in many real-world …

An agent that efficiently accumulates knowledge to develop increasingly sophisticated skills
over a long lifetime could advance the frontier of artificial intelligence capabilities. The …

This paper is in the field of stochastic Multi-Armed Bandits (MABs), ie, those sequential
selection techniques able to learn online using only the feedback given by the chosen …

In nonstationary bandit learning problems, the decision-maker must continually gather
information and adapt their action selection as the latent state of the environment evolves. In …

The Multi-Armed Bandit (MAB) problem has been extensively studied in order to address
real-world challenges related to sequential decision making. In this setting, an agent selects …

Thompson sampling has proven effective across a wide range of stationary bandit
environments. However, as we demonstrate in this paper, it can perform poorly when …

In many applications of online decision making, the environment is non-stationary and it is
therefore crucial to use bandit algorithms that handle changes. Most existing approaches …

Web-based applications such as chatbots, search engines and news recommendations
continue to grow in scale and complexity with the recent surge in the adoption of large …

There has been a recent surge of interest in non-parametric bandit algorithms based on
subsampling. One drawback however of these approaches is the additional complexity …