We conduct a comprehensive review on multi-level facility location problems which extend
several classical facility location problems and can be regarded as a subclass within the …

Optimizing multiple competing black-box objectives is a challenging problem in many fields,
including science, engineering, and machine learning. Multi-objective Bayesian optimization …

In many real-world scenarios, decision makers seek to efficiently optimize multiple
competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization …

Mobile edge computing provides the opportunity for wireless users to exploit the power of
cloud computing without a large communication delay. To serve data-intensive applications …

Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …

We study the problem of selecting a subset of big data to train a classifier while incurring
minimal performance loss. We show the connection of submodularity to the data likelihood …

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that
arise in quantum physics and random matrix theory. In contrast to traditional structured …

Peer review has become the most common practice for judging papers submitted to a
conference for decades. An extremely important task involved in peer review is to assign …

We propose a new objective function for superpixel segmentation. This objective function
consists of two components: entropy rate of a random walk on a graph and a balancing term …

Mobile edge computing is an emerging technology to offer resource-intensive yet delay-
sensitive applications from the edge of mobile networks, where a major challenge is to …