By orchestrating resources of edge and core network, the delays of edge-assisted
computing can decrease. Offloading scheduling is challenging though, especially in the …

While contextual bandit has a mature theory, effectively leveraging different feedback
patterns to enhance the pace of learning remains unclear. Bandits with feedback graphs …

We study the problem of Gaussian bandits with general side information, as first introduced
by Wu, Szepesvári, and György. In this setting, the play of an arm reveals information about …

Bandits with feedback graphs are powerful online learning models that interpolate between
the full information and classic bandit problems, capturing many real-life applications. A …

Multi-armed bandits (MAB) are widely applied to optimize networking applications such as
crowdsensing and mobile edge computing. Additional feedbacks (or partial feedbacks) on …

In this paper, we investigate the stochastic contextual bandit with general function space and
graph feedback. We propose an algorithm that addresses this problem by adapting to both …

Cascading bandit (CB) is a popular model for web search and online advertising. However,
the stationary CB model may be too simple to cope with real-world problems, where user …

In this dissertation, several topics in machine learning are presented, including sequential
prediction and decision making (eg, time series/spatiotemporal sequences prediction and …

This paper investigates the stochastic contextual bandit problem with general function space
and graph feedback. We propose a novel algorithm that effectively adapts to the time …