Abstract Model selection in the context of bandit optimization is a challenging problem, as it
requires balancing exploration and exploitation not only for action selection, but also for …

In contextual linear bandits, the reward function is assumed to be a linear combination of an
unknown reward vector and a given embedding of context-arm pairs. In practice, the …

Traditional model-free feature selection methods treat each feature independently while
disregarding the interrelationships among features, which leads to relatively poor …

In this paper, we study the problem of feature selection in distributed stochastic multi-arm
bandits, in which M agents work collaboratively to choose optimal actions under the …

Bayesian optimisation requires fitting a Gaussian process model, which in turn requires
specifying hyperparameters-most of the theoretical literature assumes those …

High-dimensional linear bandits with low-dimensional structure have received considerable
attention in recent studies due to their practical significance. The most common structure in …

Many real-world mobile robot applications, such as disaster response, military
reconnaissance, and environmental monitoring, require operating in unknown and …

Affect, encompassing both emotions and moods, plays a vital role in shaping human
experience and behavior. Emotions are intense, short-lived reactions to specific stimuli …

In our rapidly evolving technological landscape, the proliferation of enabling technologies
for autonomous systems has given rise to a burgeoning realm of societal-scale smart …

Nowadays with the growth of large-scale societal infrastructure systems, there has been
significant research attention on improving efficiency, guaranteeing safety, reducing …